Find Your Dream Deep Learning Job in India

Moleculyst Ventures Private Limited is a deep-tech biotechnology startup operating at the intersection of computational biology, artificial intelligence, and genomics, with a mission to enable precision and personalized healthcare.

We focus on modeling complex biological systems by uncovering interactions between genetics, molecular structure, and environmental factors. Our work spans AI-driven diagnostics, therapeutic discovery, and preventive healthcare, leveraging graph-based representations, large-scale biological datasets, and first-principles reasoning. Moleculyst aims to empower clinicians and researchers with scientifically grounded, next-generation computational tools.

This is a remote contract role for a Deep Learning Specialist with prior hands-on experience in Graph Neural Networks (GNNs) and a strong mathematical foundation.

The role involves designing, implementing, and optimizing deep learning models for large-scale biological data, including molecular, genomic, and interaction networks. You will work closely with interdisciplinary teams spanning biology, chemistry, and data science to develop theoretically sound and practically effective models for real-world biomedical problems.

This is not a plug-and-play ML role - we are looking for individuals who understand why models work, not just how to run them.

• Design and implement deep learning architectures, with a strong emphasis on Graph Neural Networks (GNNs) for biological and molecular data
• Develop models for molecular graphs, protein/peptide structures, interaction networks, and omics data
• Research, evaluate, and implement state-of-the-art methods (message passing, equivariant models, attention mechanisms, representation learning)
• Perform rigorous model evaluation, ablation studies, and error analysis grounded in both math and domain context
• Automate training, evaluation, and experimentation pipelines for large datasets
• Collaborate with biologists and domain experts to translate biological hypotheses into computational models
• Analyze results to extract scientifically interpretable and actionable insights

• Strong understanding of deep learning fundamentals, including:
• Linear algebra, probability, optimization
• Backpropagation, loss functions, regularization
• Bias-variance tradeoffs and generalization
• Proven experience with Graph Neural Networks, such as:
• GCN, GAT, MPNN, Graph Transformers, or related architectures
• Practical understanding of message passing, aggregation, and graph representations
• Ability to derive, reason about, and modify model formulations, not just apply libraries

• Strong proficiency in Python
• Hands-on experience with PyTorch (preferred) and/or TensorFlow
• Familiarity with ML/DL tooling for experimentation, logging, and reproducibility

• Prior experience handling biological or molecular datasets (e.g., genomics, proteomics, peptides, molecular graphs, interaction networks)
• Familiarity with bioinformatics workflows, data preprocessing, and domain constraints
• Ability to bridge biological intuition and mathematical modeling

• Experience with equivariant or physics-informed models (e.g., geometric deep learning)
• Exposure to protein language models, molecular descriptors, or structural biology data
• Experience deploying or training models on cloud platforms (AWS, GCP, Azure)
• Master's or Ph.D. in Computer Science, Bioinformatics, Computational Biology, Applied Mathematics, Physics, or related fields

• Depth over buzzwords
• Mathematical clarity over black-box usage
• Curiosity, rigor, and the ability to question assumptions
• Prior real work on GNNs, not just coursework or tutorials
• If someone cannot explain their model mathematically, they are not a fit.

Moleculyst Ventures Private Limited is a deep-tech biotechnology startup operating at the intersection of computational biology, artificial intelligence, and genomics, with a mission to enable precision and personalized healthcare.

We focus on modeling complex biological systems by uncovering interactions between genetics, molecular structure, and environmental factors. Our work spans AI-driven diagnostics, therapeutic discovery, and preventive healthcare, leveraging graph-based representations, large-scale biological datasets, and first-principles reasoning. Moleculyst aims to empower clinicians and researchers with scientifically grounded, next-generation computational tools.

This is a remote contract role for a Deep Learning Specialist with prior hands-on experience in Graph Neural Networks (GNNs) and a strong mathematical foundation.

The role involves designing, implementing, and optimizing deep learning models for large-scale biological data, including molecular, genomic, and interaction networks. You will work closely with interdisciplinary teams spanning biology, chemistry, and data science to develop theoretically sound and practically effective models for real-world biomedical problems.

This is not a plug-and-play ML role - we are looking for individuals who understand why models work, not just how to run them.

• Design and implement deep learning architectures, with a strong emphasis on Graph Neural Networks (GNNs) for biological and molecular data
• Develop models for molecular graphs, protein/peptide structures, interaction networks, and omics data
• Research, evaluate, and implement state-of-the-art methods (message passing, equivariant models, attention mechanisms, representation learning)
• Perform rigorous model evaluation, ablation studies, and error analysis grounded in both math and domain context
• Automate training, evaluation, and experimentation pipelines for large datasets
• Collaborate with biologists and domain experts to translate biological hypotheses into computational models
• Analyze results to extract scientifically interpretable and actionable insights

• Strong understanding of deep learning fundamentals, including:
• Linear algebra, probability, optimization
• Backpropagation, loss functions, regularization
• Bias-variance tradeoffs and generalization
• Proven experience with Graph Neural Networks, such as:
• GCN, GAT, MPNN, Graph Transformers, or related architectures
• Practical understanding of message passing, aggregation, and graph representations
• Ability to derive, reason about, and modify model formulations, not just apply libraries

• Strong proficiency in Python
• Hands-on experience with PyTorch (preferred) and/or TensorFlow
• Familiarity with ML/DL tooling for experimentation, logging, and reproducibility

• Prior experience handling biological or molecular datasets (e.g., genomics, proteomics, peptides, molecular graphs, interaction networks)
• Familiarity with bioinformatics workflows, data preprocessing, and domain constraints
• Ability to bridge biological intuition and mathematical modeling

• Experience with equivariant or physics-informed models (e.g., geometric deep learning)
• Exposure to protein language models, molecular descriptors, or structural biology data
• Experience deploying or training models on cloud platforms (AWS, GCP, Azure)
• Master's or Ph.D. in Computer Science, Bioinformatics, Computational Biology, Applied Mathematics, Physics, or related fields

• Depth over buzzwords
• Mathematical clarity over black-box usage
• Curiosity, rigor, and the ability to question assumptions
• Prior real work on GNNs, not just coursework or tutorials
• If someone cannot explain their model mathematically, they are not a fit.

Moleculyst Ventures Private Limited is a deep-tech biotechnology startup operating at the intersection of computational biology, artificial intelligence, and genomics, with a mission to enable precision and personalized healthcare.

We focus on modeling complex biological systems by uncovering interactions between genetics, molecular structure, and environmental factors. Our work spans AI-driven diagnostics, therapeutic discovery, and preventive healthcare, leveraging graph-based representations, large-scale biological datasets, and first-principles reasoning. Moleculyst aims to empower clinicians and researchers with scientifically grounded, next-generation computational tools.

This is a remote contract role for a Deep Learning Specialist with prior hands-on experience in Graph Neural Networks (GNNs) and a strong mathematical foundation.

The role involves designing, implementing, and optimizing deep learning models for large-scale biological data, including molecular, genomic, and interaction networks. You will work closely with interdisciplinary teams spanning biology, chemistry, and data science to develop theoretically sound and practically effective models for real-world biomedical problems.

This is not a plug-and-play ML role - we are looking for individuals who understand why models work, not just how to run them.

• Design and implement deep learning architectures, with a strong emphasis on Graph Neural Networks (GNNs) for biological and molecular data
• Develop models for molecular graphs, protein/peptide structures, interaction networks, and omics data
• Research, evaluate, and implement state-of-the-art methods (message passing, equivariant models, attention mechanisms, representation learning)
• Perform rigorous model evaluation, ablation studies, and error analysis grounded in both math and domain context
• Automate training, evaluation, and experimentation pipelines for large datasets
• Collaborate with biologists and domain experts to translate biological hypotheses into computational models
• Analyze results to extract scientifically interpretable and actionable insights

• Strong understanding of deep learning fundamentals, including:
• Linear algebra, probability, optimization
• Backpropagation, loss functions, regularization
• Bias-variance tradeoffs and generalization
• Proven experience with Graph Neural Networks, such as:
• GCN, GAT, MPNN, Graph Transformers, or related architectures
• Practical understanding of message passing, aggregation, and graph representations
• Ability to derive, reason about, and modify model formulations, not just apply libraries

• Strong proficiency in Python
• Hands-on experience with PyTorch (preferred) and/or TensorFlow
• Familiarity with ML/DL tooling for experimentation, logging, and reproducibility

• Prior experience handling biological or molecular datasets (e.g., genomics, proteomics, peptides, molecular graphs, interaction networks)
• Familiarity with bioinformatics workflows, data preprocessing, and domain constraints
• Ability to bridge biological intuition and mathematical modeling

• Experience with equivariant or physics-informed models (e.g., geometric deep learning)
• Exposure to protein language models, molecular descriptors, or structural biology data
• Experience deploying or training models on cloud platforms (AWS, GCP, Azure)
• Master's or Ph.D. in Computer Science, Bioinformatics, Computational Biology, Applied Mathematics, Physics, or related fields

• Depth over buzzwords
• Mathematical clarity over black-box usage
• Curiosity, rigor, and the ability to question assumptions
• Prior real work on GNNs, not just coursework or tutorials
• If someone cannot explain their model mathematically, they are not a fit.

Moleculyst Ventures Private Limited is a deep-tech biotechnology startup operating at the intersection of computational biology, artificial intelligence, and genomics, with a mission to enable precision and personalized healthcare.

We focus on modeling complex biological systems by uncovering interactions between genetics, molecular structure, and environmental factors. Our work spans AI-driven diagnostics, therapeutic discovery, and preventive healthcare, leveraging graph-based representations, large-scale biological datasets, and first-principles reasoning. Moleculyst aims to empower clinicians and researchers with scientifically grounded, next-generation computational tools.

This is a remote contract role for a Deep Learning Specialist with prior hands-on experience in Graph Neural Networks (GNNs) and a strong mathematical foundation.

The role involves designing, implementing, and optimizing deep learning models for large-scale biological data, including molecular, genomic, and interaction networks. You will work closely with interdisciplinary teams spanning biology, chemistry, and data science to develop theoretically sound and practically effective models for real-world biomedical problems.

This is not a plug-and-play ML role - we are looking for individuals who understand why models work, not just how to run them.

• Design and implement deep learning architectures, with a strong emphasis on Graph Neural Networks (GNNs) for biological and molecular data
• Develop models for molecular graphs, protein/peptide structures, interaction networks, and omics data
• Research, evaluate, and implement state-of-the-art methods (message passing, equivariant models, attention mechanisms, representation learning)
• Perform rigorous model evaluation, ablation studies, and error analysis grounded in both math and domain context
• Automate training, evaluation, and experimentation pipelines for large datasets
• Collaborate with biologists and domain experts to translate biological hypotheses into computational models
• Analyze results to extract scientifically interpretable and actionable insights

• Strong understanding of deep learning fundamentals, including:
• Linear algebra, probability, optimization
• Backpropagation, loss functions, regularization
• Bias-variance tradeoffs and generalization
• Proven experience with Graph Neural Networks, such as:
• GCN, GAT, MPNN, Graph Transformers, or related architectures
• Practical understanding of message passing, aggregation, and graph representations
• Ability to derive, reason about, and modify model formulations, not just apply libraries

• Strong proficiency in Python
• Hands-on experience with PyTorch (preferred) and/or TensorFlow
• Familiarity with ML/DL tooling for experimentation, logging, and reproducibility

• Prior experience handling biological or molecular datasets (e.g., genomics, proteomics, peptides, molecular graphs, interaction networks)
• Familiarity with bioinformatics workflows, data preprocessing, and domain constraints
• Ability to bridge biological intuition and mathematical modeling

• Experience with equivariant or physics-informed models (e.g., geometric deep learning)
• Exposure to protein language models, molecular descriptors, or structural biology data
• Experience deploying or training models on cloud platforms (AWS, GCP, Azure)
• Master's or Ph.D. in Computer Science, Bioinformatics, Computational Biology, Applied Mathematics, Physics, or related fields

• Depth over buzzwords
• Mathematical clarity over black-box usage
• Curiosity, rigor, and the ability to question assumptions
• Prior real work on GNNs, not just coursework or tutorials
• If someone cannot explain their model mathematically, they are not a fit.

Moleculyst Ventures Private Limited is a deep-tech biotechnology startup operating at the intersection of computational biology, artificial intelligence, and genomics, with a mission to enable precision and personalized healthcare.

We focus on modeling complex biological systems by uncovering interactions between genetics, molecular structure, and environmental factors. Our work spans AI-driven diagnostics, therapeutic discovery, and preventive healthcare, leveraging graph-based representations, large-scale biological datasets, and first-principles reasoning. Moleculyst aims to empower clinicians and researchers with scientifically grounded, next-generation computational tools.

This is a remote contract role for a Deep Learning Specialist with prior hands-on experience in Graph Neural Networks (GNNs) and a strong mathematical foundation.

The role involves designing, implementing, and optimizing deep learning models for large-scale biological data, including molecular, genomic, and interaction networks. You will work closely with interdisciplinary teams spanning biology, chemistry, and data science to develop theoretically sound and practically effective models for real-world biomedical problems.

This is not a plug-and-play ML role - we are looking for individuals who understand why models work, not just how to run them.

• Design and implement deep learning architectures, with a strong emphasis on Graph Neural Networks (GNNs) for biological and molecular data
• Develop models for molecular graphs, protein/peptide structures, interaction networks, and omics data
• Research, evaluate, and implement state-of-the-art methods (message passing, equivariant models, attention mechanisms, representation learning)
• Perform rigorous model evaluation, ablation studies, and error analysis grounded in both math and domain context
• Automate training, evaluation, and experimentation pipelines for large datasets
• Collaborate with biologists and domain experts to translate biological hypotheses into computational models
• Analyze results to extract scientifically interpretable and actionable insights

• Strong understanding of deep learning fundamentals, including:
• Linear algebra, probability, optimization
• Backpropagation, loss functions, regularization
• Bias-variance tradeoffs and generalization
• Proven experience with Graph Neural Networks, such as:
• GCN, GAT, MPNN, Graph Transformers, or related architectures
• Practical understanding of message passing, aggregation, and graph representations
• Ability to derive, reason about, and modify model formulations, not just apply libraries

• Strong proficiency in Python
• Hands-on experience with PyTorch (preferred) and/or TensorFlow
• Familiarity with ML/DL tooling for experimentation, logging, and reproducibility

• Prior experience handling biological or molecular datasets (e.g., genomics, proteomics, peptides, molecular graphs, interaction networks)
• Familiarity with bioinformatics workflows, data preprocessing, and domain constraints
• Ability to bridge biological intuition and mathematical modeling

• Experience with equivariant or physics-informed models (e.g., geometric deep learning)
• Exposure to protein language models, molecular descriptors, or structural biology data
• Experience deploying or training models on cloud platforms (AWS, GCP, Azure)
• Master's or Ph.D. in Computer Science, Bioinformatics, Computational Biology, Applied Mathematics, Physics, or related fields

• Depth over buzzwords
• Mathematical clarity over black-box usage
• Curiosity, rigor, and the ability to question assumptions
• Prior real work on GNNs, not just coursework or tutorials
• If someone cannot explain their model mathematically, they are not a fit.

Moleculyst Ventures Private Limited is a deep-tech biotechnology startup operating at the intersection of computational biology, artificial intelligence, and genomics, with a mission to enable precision and personalized healthcare.

We focus on modeling complex biological systems by uncovering interactions between genetics, molecular structure, and environmental factors. Our work spans AI-driven diagnostics, therapeutic discovery, and preventive healthcare, leveraging graph-based representations, large-scale biological datasets, and first-principles reasoning. Moleculyst aims to empower clinicians and researchers with scientifically grounded, next-generation computational tools.

This is a remote contract role for a Deep Learning Specialist with prior hands-on experience in Graph Neural Networks (GNNs) and a strong mathematical foundation.

The role involves designing, implementing, and optimizing deep learning models for large-scale biological data, including molecular, genomic, and interaction networks. You will work closely with interdisciplinary teams spanning biology, chemistry, and data science to develop theoretically sound and practically effective models for real-world biomedical problems.

This is not a plug-and-play ML role - we are looking for individuals who understand why models work, not just how to run them.

• Design and implement deep learning architectures, with a strong emphasis on Graph Neural Networks (GNNs) for biological and molecular data
• Develop models for molecular graphs, protein/peptide structures, interaction networks, and omics data
• Research, evaluate, and implement state-of-the-art methods (message passing, equivariant models, attention mechanisms, representation learning)
• Perform rigorous model evaluation, ablation studies, and error analysis grounded in both math and domain context
• Automate training, evaluation, and experimentation pipelines for large datasets
• Collaborate with biologists and domain experts to translate biological hypotheses into computational models
• Analyze results to extract scientifically interpretable and actionable insights

• Strong understanding of deep learning fundamentals, including:
• Linear algebra, probability, optimization
• Backpropagation, loss functions, regularization
• Bias-variance tradeoffs and generalization
• Proven experience with Graph Neural Networks, such as:
• GCN, GAT, MPNN, Graph Transformers, or related architectures
• Practical understanding of message passing, aggregation, and graph representations
• Ability to derive, reason about, and modify model formulations, not just apply libraries

• Strong proficiency in Python
• Hands-on experience with PyTorch (preferred) and/or TensorFlow
• Familiarity with ML/DL tooling for experimentation, logging, and reproducibility

• Prior experience handling biological or molecular datasets (e.g., genomics, proteomics, peptides, molecular graphs, interaction networks)
• Familiarity with bioinformatics workflows, data preprocessing, and domain constraints
• Ability to bridge biological intuition and mathematical modeling

• Experience with equivariant or physics-informed models (e.g., geometric deep learning)
• Exposure to protein language models, molecular descriptors, or structural biology data
• Experience deploying or training models on cloud platforms (AWS, GCP, Azure)
• Master's or Ph.D. in Computer Science, Bioinformatics, Computational Biology, Applied Mathematics, Physics, or related fields

• Depth over buzzwords
• Mathematical clarity over black-box usage
• Curiosity, rigor, and the ability to question assumptions
• Prior real work on GNNs, not just coursework or tutorials
• If someone cannot explain their model mathematically, they are not a fit.

Moleculyst Ventures Private Limited is a deep-tech biotechnology startup operating at the intersection of computational biology, artificial intelligence, and genomics, with a mission to enable precision and personalized healthcare.

We focus on modeling complex biological systems by uncovering interactions between genetics, molecular structure, and environmental factors. Our work spans AI-driven diagnostics, therapeutic discovery, and preventive healthcare, leveraging graph-based representations, large-scale biological datasets, and first-principles reasoning. Moleculyst aims to empower clinicians and researchers with scientifically grounded, next-generation computational tools.

This is a remote contract role for a Deep Learning Specialist with prior hands-on experience in Graph Neural Networks (GNNs) and a strong mathematical foundation.

The role involves designing, implementing, and optimizing deep learning models for large-scale biological data, including molecular, genomic, and interaction networks. You will work closely with interdisciplinary teams spanning biology, chemistry, and data science to develop theoretically sound and practically effective models for real-world biomedical problems.

This is not a plug-and-play ML role - we are looking for individuals who understand why models work, not just how to run them.

• Design and implement deep learning architectures, with a strong emphasis on Graph Neural Networks (GNNs) for biological and molecular data
• Develop models for molecular graphs, protein/peptide structures, interaction networks, and omics data
• Research, evaluate, and implement state-of-the-art methods (message passing, equivariant models, attention mechanisms, representation learning)
• Perform rigorous model evaluation, ablation studies, and error analysis grounded in both math and domain context
• Automate training, evaluation, and experimentation pipelines for large datasets
• Collaborate with biologists and domain experts to translate biological hypotheses into computational models
• Analyze results to extract scientifically interpretable and actionable insights

• Strong understanding of deep learning fundamentals, including:
• Linear algebra, probability, optimization
• Backpropagation, loss functions, regularization
• Bias-variance tradeoffs and generalization
• Proven experience with Graph Neural Networks, such as:
• GCN, GAT, MPNN, Graph Transformers, or related architectures
• Practical understanding of message passing, aggregation, and graph representations
• Ability to derive, reason about, and modify model formulations, not just apply libraries

• Strong proficiency in Python
• Hands-on experience with PyTorch (preferred) and/or TensorFlow
• Familiarity with ML/DL tooling for experimentation, logging, and reproducibility

• Prior experience handling biological or molecular datasets (e.g., genomics, proteomics, peptides, molecular graphs, interaction networks)
• Familiarity with bioinformatics workflows, data preprocessing, and domain constraints
• Ability to bridge biological intuition and mathematical modeling

• Experience with equivariant or physics-informed models (e.g., geometric deep learning)
• Exposure to protein language models, molecular descriptors, or structural biology data
• Experience deploying or training models on cloud platforms (AWS, GCP, Azure)
• Master's or Ph.D. in Computer Science, Bioinformatics, Computational Biology, Applied Mathematics, Physics, or related fields

• Depth over buzzwords
• Mathematical clarity over black-box usage
• Curiosity, rigor, and the ability to question assumptions
• Prior real work on GNNs, not just coursework or tutorials
• If someone cannot explain their model mathematically, they are not a fit.

Moleculyst Ventures Private Limited is a deep-tech biotechnology startup operating at the intersection of computational biology, artificial intelligence, and genomics, with a mission to enable precision and personalized healthcare.

We focus on modeling complex biological systems by uncovering interactions between genetics, molecular structure, and environmental factors. Our work spans AI-driven diagnostics, therapeutic discovery, and preventive healthcare, leveraging graph-based representations, large-scale biological datasets, and first-principles reasoning. Moleculyst aims to empower clinicians and researchers with scientifically grounded, next-generation computational tools.

This is a remote contract role for a Deep Learning Specialist with prior hands-on experience in Graph Neural Networks (GNNs) and a strong mathematical foundation.

The role involves designing, implementing, and optimizing deep learning models for large-scale biological data, including molecular, genomic, and interaction networks. You will work closely with interdisciplinary teams spanning biology, chemistry, and data science to develop theoretically sound and practically effective models for real-world biomedical problems.

This is not a plug-and-play ML role - we are looking for individuals who understand why models work, not just how to run them.

• Design and implement deep learning architectures, with a strong emphasis on Graph Neural Networks (GNNs) for biological and molecular data
• Develop models for molecular graphs, protein/peptide structures, interaction networks, and omics data
• Research, evaluate, and implement state-of-the-art methods (message passing, equivariant models, attention mechanisms, representation learning)
• Perform rigorous model evaluation, ablation studies, and error analysis grounded in both math and domain context
• Automate training, evaluation, and experimentation pipelines for large datasets
• Collaborate with biologists and domain experts to translate biological hypotheses into computational models
• Analyze results to extract scientifically interpretable and actionable insights

• Strong understanding of deep learning fundamentals, including:
• Linear algebra, probability, optimization
• Backpropagation, loss functions, regularization
• Bias-variance tradeoffs and generalization
• Proven experience with Graph Neural Networks, such as:
• GCN, GAT, MPNN, Graph Transformers, or related architectures
• Practical understanding of message passing, aggregation, and graph representations
• Ability to derive, reason about, and modify model formulations, not just apply libraries

• Strong proficiency in Python
• Hands-on experience with PyTorch (preferred) and/or TensorFlow
• Familiarity with ML/DL tooling for experimentation, logging, and reproducibility

• Prior experience handling biological or molecular datasets (e.g., genomics, proteomics, peptides, molecular graphs, interaction networks)
• Familiarity with bioinformatics workflows, data preprocessing, and domain constraints
• Ability to bridge biological intuition and mathematical modeling

• Experience with equivariant or physics-informed models (e.g., geometric deep learning)
• Exposure to protein language models, molecular descriptors, or structural biology data
• Experience deploying or training models on cloud platforms (AWS, GCP, Azure)
• Master's or Ph.D. in Computer Science, Bioinformatics, Computational Biology, Applied Mathematics, Physics, or related fields

• Depth over buzzwords
• Mathematical clarity over black-box usage
• Curiosity, rigor, and the ability to question assumptions
• Prior real work on GNNs, not just coursework or tutorials
• If someone cannot explain their model mathematically, they are not a fit.

Moleculyst Ventures Private Limited is a deep-tech biotechnology startup operating at the intersection of computational biology, artificial intelligence, and genomics, with a mission to enable precision and personalized healthcare.

We focus on modeling complex biological systems by uncovering interactions between genetics, molecular structure, and environmental factors. Our work spans AI-driven diagnostics, therapeutic discovery, and preventive healthcare, leveraging graph-based representations, large-scale biological datasets, and first-principles reasoning. Moleculyst aims to empower clinicians and researchers with scientifically grounded, next-generation computational tools.

This is a remote contract role for a Deep Learning Specialist with prior hands-on experience in Graph Neural Networks (GNNs) and a strong mathematical foundation.

The role involves designing, implementing, and optimizing deep learning models for large-scale biological data, including molecular, genomic, and interaction networks. You will work closely with interdisciplinary teams spanning biology, chemistry, and data science to develop theoretically sound and practically effective models for real-world biomedical problems.

This is not a plug-and-play ML role - we are looking for individuals who understand why models work, not just how to run them.

• Design and implement deep learning architectures, with a strong emphasis on Graph Neural Networks (GNNs) for biological and molecular data
• Develop models for molecular graphs, protein/peptide structures, interaction networks, and omics data
• Research, evaluate, and implement state-of-the-art methods (message passing, equivariant models, attention mechanisms, representation learning)
• Perform rigorous model evaluation, ablation studies, and error analysis grounded in both math and domain context
• Automate training, evaluation, and experimentation pipelines for large datasets
• Collaborate with biologists and domain experts to translate biological hypotheses into computational models
• Analyze results to extract scientifically interpretable and actionable insights

• Strong understanding of deep learning fundamentals, including:
• Linear algebra, probability, optimization
• Backpropagation, loss functions, regularization
• Bias-variance tradeoffs and generalization
• Proven experience with Graph Neural Networks, such as:
• GCN, GAT, MPNN, Graph Transformers, or related architectures
• Practical understanding of message passing, aggregation, and graph representations
• Ability to derive, reason about, and modify model formulations, not just apply libraries

• Strong proficiency in Python
• Hands-on experience with PyTorch (preferred) and/or TensorFlow
• Familiarity with ML/DL tooling for experimentation, logging, and reproducibility

• Prior experience handling biological or molecular datasets (e.g., genomics, proteomics, peptides, molecular graphs, interaction networks)
• Familiarity with bioinformatics workflows, data preprocessing, and domain constraints
• Ability to bridge biological intuition and mathematical modeling

• Experience with equivariant or physics-informed models (e.g., geometric deep learning)
• Exposure to protein language models, molecular descriptors, or structural biology data
• Experience deploying or training models on cloud platforms (AWS, GCP, Azure)
• Master's or Ph.D. in Computer Science, Bioinformatics, Computational Biology, Applied Mathematics, Physics, or related fields

• Depth over buzzwords
• Mathematical clarity over black-box usage
• Curiosity, rigor, and the ability to question assumptions
• Prior real work on GNNs, not just coursework or tutorials
• If someone cannot explain their model mathematically, they are not a fit.

Moleculyst Ventures Private Limited is a deep-tech biotechnology startup operating at the intersection of computational biology, artificial intelligence, and genomics, with a mission to enable precision and personalized healthcare.

We focus on modeling complex biological systems by uncovering interactions between genetics, molecular structure, and environmental factors. Our work spans AI-driven diagnostics, therapeutic discovery, and preventive healthcare, leveraging graph-based representations, large-scale biological datasets, and first-principles reasoning. Moleculyst aims to empower clinicians and researchers with scientifically grounded, next-generation computational tools.

This is a remote contract role for a Deep Learning Specialist with prior hands-on experience in Graph Neural Networks (GNNs) and a strong mathematical foundation.

The role involves designing, implementing, and optimizing deep learning models for large-scale biological data, including molecular, genomic, and interaction networks. You will work closely with interdisciplinary teams spanning biology, chemistry, and data science to develop theoretically sound and practically effective models for real-world biomedical problems.

This is not a plug-and-play ML role - we are looking for individuals who understand why models work, not just how to run them.

• Design and implement deep learning architectures, with a strong emphasis on Graph Neural Networks (GNNs) for biological and molecular data
• Develop models for molecular graphs, protein/peptide structures, interaction networks, and omics data
• Research, evaluate, and implement state-of-the-art methods (message passing, equivariant models, attention mechanisms, representation learning)
• Perform rigorous model evaluation, ablation studies, and error analysis grounded in both math and domain context
• Automate training, evaluation, and experimentation pipelines for large datasets
• Collaborate with biologists and domain experts to translate biological hypotheses into computational models
• Analyze results to extract scientifically interpretable and actionable insights

• Strong understanding of deep learning fundamentals, including:
• Linear algebra, probability, optimization
• Backpropagation, loss functions, regularization
• Bias-variance tradeoffs and generalization
• Proven experience with Graph Neural Networks, such as:
• GCN, GAT, MPNN, Graph Transformers, or related architectures
• Practical understanding of message passing, aggregation, and graph representations
• Ability to derive, reason about, and modify model formulations, not just apply libraries

• Strong proficiency in Python
• Hands-on experience with PyTorch (preferred) and/or TensorFlow
• Familiarity with ML/DL tooling for experimentation, logging, and reproducibility

• Prior experience handling biological or molecular datasets (e.g., genomics, proteomics, peptides, molecular graphs, interaction networks)
• Familiarity with bioinformatics workflows, data preprocessing, and domain constraints
• Ability to bridge biological intuition and mathematical modeling

• Experience with equivariant or physics-informed models (e.g., geometric deep learning)
• Exposure to protein language models, molecular descriptors, or structural biology data
• Experience deploying or training models on cloud platforms (AWS, GCP, Azure)
• Master's or Ph.D. in Computer Science, Bioinformatics, Computational Biology, Applied Mathematics, Physics, or related fields

• Depth over buzzwords
• Mathematical clarity over black-box usage
• Curiosity, rigor, and the ability to question assumptions
• Prior real work on GNNs, not just coursework or tutorials
• If someone cannot explain their model mathematically, they are not a fit.

Moleculyst Ventures Private Limited is a deep-tech biotechnology startup operating at the intersection of computational biology, artificial intelligence, and genomics, with a mission to enable precision and personalized healthcare.

We focus on modeling complex biological systems by uncovering interactions between genetics, molecular structure, and environmental factors. Our work spans AI-driven diagnostics, therapeutic discovery, and preventive healthcare, leveraging graph-based representations, large-scale biological datasets, and first-principles reasoning. Moleculyst aims to empower clinicians and researchers with scientifically grounded, next-generation computational tools.

This is a remote contract role for a Deep Learning Specialist with prior hands-on experience in Graph Neural Networks (GNNs) and a strong mathematical foundation.

The role involves designing, implementing, and optimizing deep learning models for large-scale biological data, including molecular, genomic, and interaction networks. You will work closely with interdisciplinary teams spanning biology, chemistry, and data science to develop theoretically sound and practically effective models for real-world biomedical problems.

This is not a plug-and-play ML role - we are looking for individuals who understand why models work, not just how to run them.

• Design and implement deep learning architectures, with a strong emphasis on Graph Neural Networks (GNNs) for biological and molecular data
• Develop models for molecular graphs, protein/peptide structures, interaction networks, and omics data
• Research, evaluate, and implement state-of-the-art methods (message passing, equivariant models, attention mechanisms, representation learning)
• Perform rigorous model evaluation, ablation studies, and error analysis grounded in both math and domain context
• Automate training, evaluation, and experimentation pipelines for large datasets
• Collaborate with biologists and domain experts to translate biological hypotheses into computational models
• Analyze results to extract scientifically interpretable and actionable insights

• Strong understanding of deep learning fundamentals, including:
• Linear algebra, probability, optimization
• Backpropagation, loss functions, regularization
• Bias-variance tradeoffs and generalization
• Proven experience with Graph Neural Networks, such as:
• GCN, GAT, MPNN, Graph Transformers, or related architectures
• Practical understanding of message passing, aggregation, and graph representations
• Ability to derive, reason about, and modify model formulations, not just apply libraries

• Strong proficiency in Python
• Hands-on experience with PyTorch (preferred) and/or TensorFlow
• Familiarity with ML/DL tooling for experimentation, logging, and reproducibility

• Prior experience handling biological or molecular datasets (e.g., genomics, proteomics, peptides, molecular graphs, interaction networks)
• Familiarity with bioinformatics workflows, data preprocessing, and domain constraints
• Ability to bridge biological intuition and mathematical modeling

• Experience with equivariant or physics-informed models (e.g., geometric deep learning)
• Exposure to protein language models, molecular descriptors, or structural biology data
• Experience deploying or training models on cloud platforms (AWS, GCP, Azure)
• Master's or Ph.D. in Computer Science, Bioinformatics, Computational Biology, Applied Mathematics, Physics, or related fields

• Depth over buzzwords
• Mathematical clarity over black-box usage
• Curiosity, rigor, and the ability to question assumptions
• Prior real work on GNNs, not just coursework or tutorials
• If someone cannot explain their model mathematically, they are not a fit.

Moleculyst Ventures Private Limited is a deep-tech biotechnology startup operating at the intersection of computational biology, artificial intelligence, and genomics, with a mission to enable precision and personalized healthcare.

We focus on modeling complex biological systems by uncovering interactions between genetics, molecular structure, and environmental factors. Our work spans AI-driven diagnostics, therapeutic discovery, and preventive healthcare, leveraging graph-based representations, large-scale biological datasets, and first-principles reasoning. Moleculyst aims to empower clinicians and researchers with scientifically grounded, next-generation computational tools.

This is a remote contract role for a Deep Learning Specialist with prior hands-on experience in Graph Neural Networks (GNNs) and a strong mathematical foundation.

The role involves designing, implementing, and optimizing deep learning models for large-scale biological data, including molecular, genomic, and interaction networks. You will work closely with interdisciplinary teams spanning biology, chemistry, and data science to develop theoretically sound and practically effective models for real-world biomedical problems.

This is not a plug-and-play ML role - we are looking for individuals who understand why models work, not just how to run them.

• Design and implement deep learning architectures, with a strong emphasis on Graph Neural Networks (GNNs) for biological and molecular data
• Develop models for molecular graphs, protein/peptide structures, interaction networks, and omics data
• Research, evaluate, and implement state-of-the-art methods (message passing, equivariant models, attention mechanisms, representation learning)
• Perform rigorous model evaluation, ablation studies, and error analysis grounded in both math and domain context
• Automate training, evaluation, and experimentation pipelines for large datasets
• Collaborate with biologists and domain experts to translate biological hypotheses into computational models
• Analyze results to extract scientifically interpretable and actionable insights

• Strong understanding of deep learning fundamentals, including:
• Linear algebra, probability, optimization
• Backpropagation, loss functions, regularization
• Bias-variance tradeoffs and generalization
• Proven experience with Graph Neural Networks, such as:
• GCN, GAT, MPNN, Graph Transformers, or related architectures
• Practical understanding of message passing, aggregation, and graph representations
• Ability to derive, reason about, and modify model formulations, not just apply libraries

• Strong proficiency in Python
• Hands-on experience with PyTorch (preferred) and/or TensorFlow
• Familiarity with ML/DL tooling for experimentation, logging, and reproducibility

• Prior experience handling biological or molecular datasets (e.g., genomics, proteomics, peptides, molecular graphs, interaction networks)
• Familiarity with bioinformatics workflows, data preprocessing, and domain constraints
• Ability to bridge biological intuition and mathematical modeling

• Experience with equivariant or physics-informed models (e.g., geometric deep learning)
• Exposure to protein language models, molecular descriptors, or structural biology data
• Experience deploying or training models on cloud platforms (AWS, GCP, Azure)
• Master's or Ph.D. in Computer Science, Bioinformatics, Computational Biology, Applied Mathematics, Physics, or related fields

• Depth over buzzwords
• Mathematical clarity over black-box usage
• Curiosity, rigor, and the ability to question assumptions
• Prior real work on GNNs, not just coursework or tutorials
• If someone cannot explain their model mathematically, they are not a fit.

Moleculyst Ventures Private Limited is a deep-tech biotechnology startup operating at the intersection of computational biology, artificial intelligence, and genomics, with a mission to enable precision and personalized healthcare.

We focus on modeling complex biological systems by uncovering interactions between genetics, molecular structure, and environmental factors. Our work spans AI-driven diagnostics, therapeutic discovery, and preventive healthcare, leveraging graph-based representations, large-scale biological datasets, and first-principles reasoning. Moleculyst aims to empower clinicians and researchers with scientifically grounded, next-generation computational tools.

This is a remote contract role for a Deep Learning Specialist with prior hands-on experience in Graph Neural Networks (GNNs) and a strong mathematical foundation.

The role involves designing, implementing, and optimizing deep learning models for large-scale biological data, including molecular, genomic, and interaction networks. You will work closely with interdisciplinary teams spanning biology, chemistry, and data science to develop theoretically sound and practically effective models for real-world biomedical problems.

This is not a plug-and-play ML role - we are looking for individuals who understand why models work, not just how to run them.

• Design and implement deep learning architectures, with a strong emphasis on Graph Neural Networks (GNNs) for biological and molecular data
• Develop models for molecular graphs, protein/peptide structures, interaction networks, and omics data
• Research, evaluate, and implement state-of-the-art methods (message passing, equivariant models, attention mechanisms, representation learning)
• Perform rigorous model evaluation, ablation studies, and error analysis grounded in both math and domain context
• Automate training, evaluation, and experimentation pipelines for large datasets
• Collaborate with biologists and domain experts to translate biological hypotheses into computational models
• Analyze results to extract scientifically interpretable and actionable insights

• Strong understanding of deep learning fundamentals, including:
• Linear algebra, probability, optimization
• Backpropagation, loss functions, regularization
• Bias-variance tradeoffs and generalization
• Proven experience with Graph Neural Networks, such as:
• GCN, GAT, MPNN, Graph Transformers, or related architectures
• Practical understanding of message passing, aggregation, and graph representations
• Ability to derive, reason about, and modify model formulations, not just apply libraries

• Strong proficiency in Python
• Hands-on experience with PyTorch (preferred) and/or TensorFlow
• Familiarity with ML/DL tooling for experimentation, logging, and reproducibility

• Prior experience handling biological or molecular datasets (e.g., genomics, proteomics, peptides, molecular graphs, interaction networks)
• Familiarity with bioinformatics workflows, data preprocessing, and domain constraints
• Ability to bridge biological intuition and mathematical modeling

• Experience with equivariant or physics-informed models (e.g., geometric deep learning)
• Exposure to protein language models, molecular descriptors, or structural biology data
• Experience deploying or training models on cloud platforms (AWS, GCP, Azure)
• Master's or Ph.D. in Computer Science, Bioinformatics, Computational Biology, Applied Mathematics, Physics, or related fields

• Depth over buzzwords
• Mathematical clarity over black-box usage
• Curiosity, rigor, and the ability to question assumptions
• Prior real work on GNNs, not just coursework or tutorials
• If someone cannot explain their model mathematically, they are not a fit.

Moleculyst Ventures Private Limited is a deep-tech biotechnology startup operating at the intersection of computational biology, artificial intelligence, and genomics, with a mission to enable precision and personalized healthcare.

We focus on modeling complex biological systems by uncovering interactions between genetics, molecular structure, and environmental factors. Our work spans AI-driven diagnostics, therapeutic discovery, and preventive healthcare, leveraging graph-based representations, large-scale biological datasets, and first-principles reasoning. Moleculyst aims to empower clinicians and researchers with scientifically grounded, next-generation computational tools.

This is a remote contract role for a Deep Learning Specialist with prior hands-on experience in Graph Neural Networks (GNNs) and a strong mathematical foundation.

The role involves designing, implementing, and optimizing deep learning models for large-scale biological data, including molecular, genomic, and interaction networks. You will work closely with interdisciplinary teams spanning biology, chemistry, and data science to develop theoretically sound and practically effective models for real-world biomedical problems.

This is not a plug-and-play ML role - we are looking for individuals who understand why models work, not just how to run them.

• Design and implement deep learning architectures, with a strong emphasis on Graph Neural Networks (GNNs) for biological and molecular data
• Develop models for molecular graphs, protein/peptide structures, interaction networks, and omics data
• Research, evaluate, and implement state-of-the-art methods (message passing, equivariant models, attention mechanisms, representation learning)
• Perform rigorous model evaluation, ablation studies, and error analysis grounded in both math and domain context
• Automate training, evaluation, and experimentation pipelines for large datasets
• Collaborate with biologists and domain experts to translate biological hypotheses into computational models
• Analyze results to extract scientifically interpretable and actionable insights

• Strong understanding of deep learning fundamentals, including:
• Linear algebra, probability, optimization
• Backpropagation, loss functions, regularization
• Bias-variance tradeoffs and generalization
• Proven experience with Graph Neural Networks, such as:
• GCN, GAT, MPNN, Graph Transformers, or related architectures
• Practical understanding of message passing, aggregation, and graph representations
• Ability to derive, reason about, and modify model formulations, not just apply libraries

• Strong proficiency in Python
• Hands-on experience with PyTorch (preferred) and/or TensorFlow
• Familiarity with ML/DL tooling for experimentation, logging, and reproducibility

• Prior experience handling biological or molecular datasets (e.g., genomics, proteomics, peptides, molecular graphs, interaction networks)
• Familiarity with bioinformatics workflows, data preprocessing, and domain constraints
• Ability to bridge biological intuition and mathematical modeling

• Experience with equivariant or physics-informed models (e.g., geometric deep learning)
• Exposure to protein language models, molecular descriptors, or structural biology data
• Experience deploying or training models on cloud platforms (AWS, GCP, Azure)
• Master's or Ph.D. in Computer Science, Bioinformatics, Computational Biology, Applied Mathematics, Physics, or related fields

• Depth over buzzwords
• Mathematical clarity over black-box usage
• Curiosity, rigor, and the ability to question assumptions
• Prior real work on GNNs, not just coursework or tutorials
• If someone cannot explain their model mathematically, they are not a fit.

Moleculyst Ventures Private Limited is a deep-tech biotechnology startup operating at the intersection of computational biology, artificial intelligence, and genomics, with a mission to enable precision and personalized healthcare.

We focus on modeling complex biological systems by uncovering interactions between genetics, molecular structure, and environmental factors. Our work spans AI-driven diagnostics, therapeutic discovery, and preventive healthcare, leveraging graph-based representations, large-scale biological datasets, and first-principles reasoning. Moleculyst aims to empower clinicians and researchers with scientifically grounded, next-generation computational tools.

This is a remote contract role for a Deep Learning Specialist with prior hands-on experience in Graph Neural Networks (GNNs) and a strong mathematical foundation.

The role involves designing, implementing, and optimizing deep learning models for large-scale biological data, including molecular, genomic, and interaction networks. You will work closely with interdisciplinary teams spanning biology, chemistry, and data science to develop theoretically sound and practically effective models for real-world biomedical problems.

This is not a plug-and-play ML role - we are looking for individuals who understand why models work, not just how to run them.

• Design and implement deep learning architectures, with a strong emphasis on Graph Neural Networks (GNNs) for biological and molecular data
• Develop models for molecular graphs, protein/peptide structures, interaction networks, and omics data
• Research, evaluate, and implement state-of-the-art methods (message passing, equivariant models, attention mechanisms, representation learning)
• Perform rigorous model evaluation, ablation studies, and error analysis grounded in both math and domain context
• Automate training, evaluation, and experimentation pipelines for large datasets
• Collaborate with biologists and domain experts to translate biological hypotheses into computational models
• Analyze results to extract scientifically interpretable and actionable insights

• Strong understanding of deep learning fundamentals, including:
• Linear algebra, probability, optimization
• Backpropagation, loss functions, regularization
• Bias-variance tradeoffs and generalization
• Proven experience with Graph Neural Networks, such as:
• GCN, GAT, MPNN, Graph Transformers, or related architectures
• Practical understanding of message passing, aggregation, and graph representations
• Ability to derive, reason about, and modify model formulations, not just apply libraries

• Strong proficiency in Python
• Hands-on experience with PyTorch (preferred) and/or TensorFlow
• Familiarity with ML/DL tooling for experimentation, logging, and reproducibility

• Prior experience handling biological or molecular datasets (e.g., genomics, proteomics, peptides, molecular graphs, interaction networks)
• Familiarity with bioinformatics workflows, data preprocessing, and domain constraints
• Ability to bridge biological intuition and mathematical modeling

• Experience with equivariant or physics-informed models (e.g., geometric deep learning)
• Exposure to protein language models, molecular descriptors, or structural biology data
• Experience deploying or training models on cloud platforms (AWS, GCP, Azure)
• Master's or Ph.D. in Computer Science, Bioinformatics, Computational Biology, Applied Mathematics, Physics, or related fields

• Depth over buzzwords
• Mathematical clarity over black-box usage
• Curiosity, rigor, and the ability to question assumptions
• Prior real work on GNNs, not just coursework or tutorials
• If someone cannot explain their model mathematically, they are not a fit.

Moleculyst Ventures Private Limited is a deep-tech biotechnology startup operating at the intersection of computational biology, artificial intelligence, and genomics, with a mission to enable precision and personalized healthcare.

We focus on modeling complex biological systems by uncovering interactions between genetics, molecular structure, and environmental factors. Our work spans AI-driven diagnostics, therapeutic discovery, and preventive healthcare, leveraging graph-based representations, large-scale biological datasets, and first-principles reasoning. Moleculyst aims to empower clinicians and researchers with scientifically grounded, next-generation computational tools.

This is a remote contract role for a Deep Learning Specialist with prior hands-on experience in Graph Neural Networks (GNNs) and a strong mathematical foundation.

The role involves designing, implementing, and optimizing deep learning models for large-scale biological data, including molecular, genomic, and interaction networks. You will work closely with interdisciplinary teams spanning biology, chemistry, and data science to develop theoretically sound and practically effective models for real-world biomedical problems.

This is not a plug-and-play ML role - we are looking for individuals who understand why models work, not just how to run them.

• Design and implement deep learning architectures, with a strong emphasis on Graph Neural Networks (GNNs) for biological and molecular data
• Develop models for molecular graphs, protein/peptide structures, interaction networks, and omics data
• Research, evaluate, and implement state-of-the-art methods (message passing, equivariant models, attention mechanisms, representation learning)
• Perform rigorous model evaluation, ablation studies, and error analysis grounded in both math and domain context
• Automate training, evaluation, and experimentation pipelines for large datasets
• Collaborate with biologists and domain experts to translate biological hypotheses into computational models
• Analyze results to extract scientifically interpretable and actionable insights

• Strong understanding of deep learning fundamentals, including:
• Linear algebra, probability, optimization
• Backpropagation, loss functions, regularization
• Bias-variance tradeoffs and generalization
• Proven experience with Graph Neural Networks, such as:
• GCN, GAT, MPNN, Graph Transformers, or related architectures
• Practical understanding of message passing, aggregation, and graph representations
• Ability to derive, reason about, and modify model formulations, not just apply libraries

• Strong proficiency in Python
• Hands-on experience with PyTorch (preferred) and/or TensorFlow
• Familiarity with ML/DL tooling for experimentation, logging, and reproducibility

• Prior experience handling biological or molecular datasets (e.g., genomics, proteomics, peptides, molecular graphs, interaction networks)
• Familiarity with bioinformatics workflows, data preprocessing, and domain constraints
• Ability to bridge biological intuition and mathematical modeling

• Experience with equivariant or physics-informed models (e.g., geometric deep learning)
• Exposure to protein language models, molecular descriptors, or structural biology data
• Experience deploying or training models on cloud platforms (AWS, GCP, Azure)
• Master's or Ph.D. in Computer Science, Bioinformatics, Computational Biology, Applied Mathematics, Physics, or related fields

• Depth over buzzwords
• Mathematical clarity over black-box usage
• Curiosity, rigor, and the ability to question assumptions
• Prior real work on GNNs, not just coursework or tutorials
• If someone cannot explain their model mathematically, they are not a fit.

Moleculyst Ventures Private Limited is a deep-tech biotechnology startup operating at the intersection of computational biology, artificial intelligence, and genomics, with a mission to enable precision and personalized healthcare.

We focus on modeling complex biological systems by uncovering interactions between genetics, molecular structure, and environmental factors. Our work spans AI-driven diagnostics, therapeutic discovery, and preventive healthcare, leveraging graph-based representations, large-scale biological datasets, and first-principles reasoning. Moleculyst aims to empower clinicians and researchers with scientifically grounded, next-generation computational tools.

This is a remote contract role for a Deep Learning Specialist with prior hands-on experience in Graph Neural Networks (GNNs) and a strong mathematical foundation.

The role involves designing, implementing, and optimizing deep learning models for large-scale biological data, including molecular, genomic, and interaction networks. You will work closely with interdisciplinary teams spanning biology, chemistry, and data science to develop theoretically sound and practically effective models for real-world biomedical problems.

This is not a plug-and-play ML role - we are looking for individuals who understand why models work, not just how to run them.

• Design and implement deep learning architectures, with a strong emphasis on Graph Neural Networks (GNNs) for biological and molecular data
• Develop models for molecular graphs, protein/peptide structures, interaction networks, and omics data
• Research, evaluate, and implement state-of-the-art methods (message passing, equivariant models, attention mechanisms, representation learning)
• Perform rigorous model evaluation, ablation studies, and error analysis grounded in both math and domain context
• Automate training, evaluation, and experimentation pipelines for large datasets
• Collaborate with biologists and domain experts to translate biological hypotheses into computational models
• Analyze results to extract scientifically interpretable and actionable insights

• Strong understanding of deep learning fundamentals, including:
• Linear algebra, probability, optimization
• Backpropagation, loss functions, regularization
• Bias-variance tradeoffs and generalization
• Proven experience with Graph Neural Networks, such as:
• GCN, GAT, MPNN, Graph Transformers, or related architectures
• Practical understanding of message passing, aggregation, and graph representations
• Ability to derive, reason about, and modify model formulations, not just apply libraries

• Strong proficiency in Python
• Hands-on experience with PyTorch (preferred) and/or TensorFlow
• Familiarity with ML/DL tooling for experimentation, logging, and reproducibility

• Prior experience handling biological or molecular datasets (e.g., genomics, proteomics, peptides, molecular graphs, interaction networks)
• Familiarity with bioinformatics workflows, data preprocessing, and domain constraints
• Ability to bridge biological intuition and mathematical modeling

• Experience with equivariant or physics-informed models (e.g., geometric deep learning)
• Exposure to protein language models, molecular descriptors, or structural biology data
• Experience deploying or training models on cloud platforms (AWS, GCP, Azure)
• Master's or Ph.D. in Computer Science, Bioinformatics, Computational Biology, Applied Mathematics, Physics, or related fields

• Depth over buzzwords
• Mathematical clarity over black-box usage
• Curiosity, rigor, and the ability to question assumptions
• Prior real work on GNNs, not just coursework or tutorials
• If someone cannot explain their model mathematically, they are not a fit.

Moleculyst Ventures Private Limited is a deep-tech biotechnology startup operating at the intersection of computational biology, artificial intelligence, and genomics, with a mission to enable precision and personalized healthcare.

We focus on modeling complex biological systems by uncovering interactions between genetics, molecular structure, and environmental factors. Our work spans AI-driven diagnostics, therapeutic discovery, and preventive healthcare, leveraging graph-based representations, large-scale biological datasets, and first-principles reasoning. Moleculyst aims to empower clinicians and researchers with scientifically grounded, next-generation computational tools.

This is a remote contract role for a Deep Learning Specialist with prior hands-on experience in Graph Neural Networks (GNNs) and a strong mathematical foundation.

The role involves designing, implementing, and optimizing deep learning models for large-scale biological data, including molecular, genomic, and interaction networks. You will work closely with interdisciplinary teams spanning biology, chemistry, and data science to develop theoretically sound and practically effective models for real-world biomedical problems.

This is not a plug-and-play ML role - we are looking for individuals who understand why models work, not just how to run them.

• Design and implement deep learning architectures, with a strong emphasis on Graph Neural Networks (GNNs) for biological and molecular data
• Develop models for molecular graphs, protein/peptide structures, interaction networks, and omics data
• Research, evaluate, and implement state-of-the-art methods (message passing, equivariant models, attention mechanisms, representation learning)
• Perform rigorous model evaluation, ablation studies, and error analysis grounded in both math and domain context
• Automate training, evaluation, and experimentation pipelines for large datasets
• Collaborate with biologists and domain experts to translate biological hypotheses into computational models
• Analyze results to extract scientifically interpretable and actionable insights

• Strong understanding of deep learning fundamentals, including:
• Linear algebra, probability, optimization
• Backpropagation, loss functions, regularization
• Bias-variance tradeoffs and generalization
• Proven experience with Graph Neural Networks, such as:
• GCN, GAT, MPNN, Graph Transformers, or related architectures
• Practical understanding of message passing, aggregation, and graph representations
• Ability to derive, reason about, and modify model formulations, not just apply libraries

• Strong proficiency in Python
• Hands-on experience with PyTorch (preferred) and/or TensorFlow
• Familiarity with ML/DL tooling for experimentation, logging, and reproducibility

• Prior experience handling biological or molecular datasets (e.g., genomics, proteomics, peptides, molecular graphs, interaction networks)
• Familiarity with bioinformatics workflows, data preprocessing, and domain constraints
• Ability to bridge biological intuition and mathematical modeling

• Experience with equivariant or physics-informed models (e.g., geometric deep learning)
• Exposure to protein language models, molecular descriptors, or structural biology data
• Experience deploying or training models on cloud platforms (AWS, GCP, Azure)
• Master's or Ph.D. in Computer Science, Bioinformatics, Computational Biology, Applied Mathematics, Physics, or related fields

• Depth over buzzwords
• Mathematical clarity over black-box usage
• Curiosity, rigor, and the ability to question assumptions
• Prior real work on GNNs, not just coursework or tutorials
• If someone cannot explain their model mathematically, they are not a fit.

Moleculyst Ventures Private Limited is a deep-tech biotechnology startup operating at the intersection of computational biology, artificial intelligence, and genomics, with a mission to enable precision and personalized healthcare.

We focus on modeling complex biological systems by uncovering interactions between genetics, molecular structure, and environmental factors. Our work spans AI-driven diagnostics, therapeutic discovery, and preventive healthcare, leveraging graph-based representations, large-scale biological datasets, and first-principles reasoning. Moleculyst aims to empower clinicians and researchers with scientifically grounded, next-generation computational tools.

This is a remote contract role for a Deep Learning Specialist with prior hands-on experience in Graph Neural Networks (GNNs) and a strong mathematical foundation.

The role involves designing, implementing, and optimizing deep learning models for large-scale biological data, including molecular, genomic, and interaction networks. You will work closely with interdisciplinary teams spanning biology, chemistry, and data science to develop theoretically sound and practically effective models for real-world biomedical problems.

This is not a plug-and-play ML role - we are looking for individuals who understand why models work, not just how to run them.

• Design and implement deep learning architectures, with a strong emphasis on Graph Neural Networks (GNNs) for biological and molecular data
• Develop models for molecular graphs, protein/peptide structures, interaction networks, and omics data
• Research, evaluate, and implement state-of-the-art methods (message passing, equivariant models, attention mechanisms, representation learning)
• Perform rigorous model evaluation, ablation studies, and error analysis grounded in both math and domain context
• Automate training, evaluation, and experimentation pipelines for large datasets
• Collaborate with biologists and domain experts to translate biological hypotheses into computational models
• Analyze results to extract scientifically interpretable and actionable insights

• Strong understanding of deep learning fundamentals, including:
• Linear algebra, probability, optimization
• Backpropagation, loss functions, regularization
• Bias-variance tradeoffs and generalization
• Proven experience with Graph Neural Networks, such as:
• GCN, GAT, MPNN, Graph Transformers, or related architectures
• Practical understanding of message passing, aggregation, and graph representations
• Ability to derive, reason about, and modify model formulations, not just apply libraries

• Strong proficiency in Python
• Hands-on experience with PyTorch (preferred) and/or TensorFlow
• Familiarity with ML/DL tooling for experimentation, logging, and reproducibility

• Prior experience handling biological or molecular datasets (e.g., genomics, proteomics, peptides, molecular graphs, interaction networks)
• Familiarity with bioinformatics workflows, data preprocessing, and domain constraints
• Ability to bridge biological intuition and mathematical modeling

• Experience with equivariant or physics-informed models (e.g., geometric deep learning)
• Exposure to protein language models, molecular descriptors, or structural biology data
• Experience deploying or training models on cloud platforms (AWS, GCP, Azure)
• Master's or Ph.D. in Computer Science, Bioinformatics, Computational Biology, Applied Mathematics, Physics, or related fields

• Depth over buzzwords
• Mathematical clarity over black-box usage
• Curiosity, rigor, and the ability to question assumptions
• Prior real work on GNNs, not just coursework or tutorials
• If someone cannot explain their model mathematically, they are not a fit.

Moleculyst Ventures Private Limited is a deep-tech biotechnology startup operating at the intersection of computational biology, artificial intelligence, and genomics, with a mission to enable precision and personalized healthcare.

We focus on modeling complex biological systems by uncovering interactions between genetics, molecular structure, and environmental factors. Our work spans AI-driven diagnostics, therapeutic discovery, and preventive healthcare, leveraging graph-based representations, large-scale biological datasets, and first-principles reasoning. Moleculyst aims to empower clinicians and researchers with scientifically grounded, next-generation computational tools.

This is a remote contract role for a Deep Learning Specialist with prior hands-on experience in Graph Neural Networks (GNNs) and a strong mathematical foundation.

The role involves designing, implementing, and optimizing deep learning models for large-scale biological data, including molecular, genomic, and interaction networks. You will work closely with interdisciplinary teams spanning biology, chemistry, and data science to develop theoretically sound and practically effective models for real-world biomedical problems.

This is not a plug-and-play ML role - we are looking for individuals who understand why models work, not just how to run them.

• Design and implement deep learning architectures, with a strong emphasis on Graph Neural Networks (GNNs) for biological and molecular data
• Develop models for molecular graphs, protein/peptide structures, interaction networks, and omics data
• Research, evaluate, and implement state-of-the-art methods (message passing, equivariant models, attention mechanisms, representation learning)
• Perform rigorous model evaluation, ablation studies, and error analysis grounded in both math and domain context
• Automate training, evaluation, and experimentation pipelines for large datasets
• Collaborate with biologists and domain experts to translate biological hypotheses into computational models
• Analyze results to extract scientifically interpretable and actionable insights

• Strong understanding of deep learning fundamentals, including:
• Linear algebra, probability, optimization
• Backpropagation, loss functions, regularization
• Bias-variance tradeoffs and generalization
• Proven experience with Graph Neural Networks, such as:
• GCN, GAT, MPNN, Graph Transformers, or related architectures
• Practical understanding of message passing, aggregation, and graph representations
• Ability to derive, reason about, and modify model formulations, not just apply libraries

• Strong proficiency in Python
• Hands-on experience with PyTorch (preferred) and/or TensorFlow
• Familiarity with ML/DL tooling for experimentation, logging, and reproducibility

• Prior experience handling biological or molecular datasets (e.g., genomics, proteomics, peptides, molecular graphs, interaction networks)
• Familiarity with bioinformatics workflows, data preprocessing, and domain constraints
• Ability to bridge biological intuition and mathematical modeling

• Experience with equivariant or physics-informed models (e.g., geometric deep learning)
• Exposure to protein language models, molecular descriptors, or structural biology data
• Experience deploying or training models on cloud platforms (AWS, GCP, Azure)
• Master's or Ph.D. in Computer Science, Bioinformatics, Computational Biology, Applied Mathematics, Physics, or related fields

• Depth over buzzwords
• Mathematical clarity over black-box usage
• Curiosity, rigor, and the ability to question assumptions
• Prior real work on GNNs, not just coursework or tutorials
• If someone cannot explain their model mathematically, they are not a fit.

Moleculyst Ventures Private Limited is a deep-tech biotechnology startup operating at the intersection of computational biology, artificial intelligence, and genomics, with a mission to enable precision and personalized healthcare.

We focus on modeling complex biological systems by uncovering interactions between genetics, molecular structure, and environmental factors. Our work spans AI-driven diagnostics, therapeutic discovery, and preventive healthcare, leveraging graph-based representations, large-scale biological datasets, and first-principles reasoning. Moleculyst aims to empower clinicians and researchers with scientifically grounded, next-generation computational tools.

This is a remote contract role for a Deep Learning Specialist with prior hands-on experience in Graph Neural Networks (GNNs) and a strong mathematical foundation.

The role involves designing, implementing, and optimizing deep learning models for large-scale biological data, including molecular, genomic, and interaction networks. You will work closely with interdisciplinary teams spanning biology, chemistry, and data science to develop theoretically sound and practically effective models for real-world biomedical problems.

This is not a plug-and-play ML role - we are looking for individuals who understand why models work, not just how to run them.

• Design and implement deep learning architectures, with a strong emphasis on Graph Neural Networks (GNNs) for biological and molecular data
• Develop models for molecular graphs, protein/peptide structures, interaction networks, and omics data
• Research, evaluate, and implement state-of-the-art methods (message passing, equivariant models, attention mechanisms, representation learning)
• Perform rigorous model evaluation, ablation studies, and error analysis grounded in both math and domain context
• Automate training, evaluation, and experimentation pipelines for large datasets
• Collaborate with biologists and domain experts to translate biological hypotheses into computational models
• Analyze results to extract scientifically interpretable and actionable insights

• Strong understanding of deep learning fundamentals, including:
• Linear algebra, probability, optimization
• Backpropagation, loss functions, regularization
• Bias-variance tradeoffs and generalization
• Proven experience with Graph Neural Networks, such as:
• GCN, GAT, MPNN, Graph Transformers, or related architectures
• Practical understanding of message passing, aggregation, and graph representations
• Ability to derive, reason about, and modify model formulations, not just apply libraries

• Strong proficiency in Python
• Hands-on experience with PyTorch (preferred) and/or TensorFlow
• Familiarity with ML/DL tooling for experimentation, logging, and reproducibility

• Prior experience handling biological or molecular datasets (e.g., genomics, proteomics, peptides, molecular graphs, interaction networks)
• Familiarity with bioinformatics workflows, data preprocessing, and domain constraints
• Ability to bridge biological intuition and mathematical modeling

• Experience with equivariant or physics-informed models (e.g., geometric deep learning)
• Exposure to protein language models, molecular descriptors, or structural biology data
• Experience deploying or training models on cloud platforms (AWS, GCP, Azure)
• Master's or Ph.D. in Computer Science, Bioinformatics, Computational Biology, Applied Mathematics, Physics, or related fields

• Depth over buzzwords
• Mathematical clarity over black-box usage
• Curiosity, rigor, and the ability to question assumptions
• Prior real work on GNNs, not just coursework or tutorials
• If someone cannot explain their model mathematically, they are not a fit.

Moleculyst Ventures Private Limited is a deep-tech biotechnology startup operating at the intersection of computational biology, artificial intelligence, and genomics, with a mission to enable precision and personalized healthcare.

We focus on modeling complex biological systems by uncovering interactions between genetics, molecular structure, and environmental factors. Our work spans AI-driven diagnostics, therapeutic discovery, and preventive healthcare, leveraging graph-based representations, large-scale biological datasets, and first-principles reasoning. Moleculyst aims to empower clinicians and researchers with scientifically grounded, next-generation computational tools.

This is a remote contract role for a Deep Learning Specialist with prior hands-on experience in Graph Neural Networks (GNNs) and a strong mathematical foundation.

The role involves designing, implementing, and optimizing deep learning models for large-scale biological data, including molecular, genomic, and interaction networks. You will work closely with interdisciplinary teams spanning biology, chemistry, and data science to develop theoretically sound and practically effective models for real-world biomedical problems.

This is not a plug-and-play ML role - we are looking for individuals who understand why models work, not just how to run them.

• Design and implement deep learning architectures, with a strong emphasis on Graph Neural Networks (GNNs) for biological and molecular data
• Develop models for molecular graphs, protein/peptide structures, interaction networks, and omics data
• Research, evaluate, and implement state-of-the-art methods (message passing, equivariant models, attention mechanisms, representation learning)
• Perform rigorous model evaluation, ablation studies, and error analysis grounded in both math and domain context
• Automate training, evaluation, and experimentation pipelines for large datasets
• Collaborate with biologists and domain experts to translate biological hypotheses into computational models
• Analyze results to extract scientifically interpretable and actionable insights

• Strong understanding of deep learning fundamentals, including:
• Linear algebra, probability, optimization
• Backpropagation, loss functions, regularization
• Bias-variance tradeoffs and generalization
• Proven experience with Graph Neural Networks, such as:
• GCN, GAT, MPNN, Graph Transformers, or related architectures
• Practical understanding of message passing, aggregation, and graph representations
• Ability to derive, reason about, and modify model formulations, not just apply libraries

• Strong proficiency in Python
• Hands-on experience with PyTorch (preferred) and/or TensorFlow
• Familiarity with ML/DL tooling for experimentation, logging, and reproducibility

• Prior experience handling biological or molecular datasets (e.g., genomics, proteomics, peptides, molecular graphs, interaction networks)
• Familiarity with bioinformatics workflows, data preprocessing, and domain constraints
• Ability to bridge biological intuition and mathematical modeling

• Experience with equivariant or physics-informed models (e.g., geometric deep learning)
• Exposure to protein language models, molecular descriptors, or structural biology data
• Experience deploying or training models on cloud platforms (AWS, GCP, Azure)
• Master's or Ph.D. in Computer Science, Bioinformatics, Computational Biology, Applied Mathematics, Physics, or related fields

• Depth over buzzwords
• Mathematical clarity over black-box usage
• Curiosity, rigor, and the ability to question assumptions
• Prior real work on GNNs, not just coursework or tutorials
• If someone cannot explain their model mathematically, they are not a fit.

Moleculyst Ventures Private Limited is a deep-tech biotechnology startup operating at the intersection of computational biology, artificial intelligence, and genomics, with a mission to enable precision and personalized healthcare.

We focus on modeling complex biological systems by uncovering interactions between genetics, molecular structure, and environmental factors. Our work spans AI-driven diagnostics, therapeutic discovery, and preventive healthcare, leveraging graph-based representations, large-scale biological datasets, and first-principles reasoning. Moleculyst aims to empower clinicians and researchers with scientifically grounded, next-generation computational tools.

This is a remote contract role for a Deep Learning Specialist with prior hands-on experience in Graph Neural Networks (GNNs) and a strong mathematical foundation.

The role involves designing, implementing, and optimizing deep learning models for large-scale biological data, including molecular, genomic, and interaction networks. You will work closely with interdisciplinary teams spanning biology, chemistry, and data science to develop theoretically sound and practically effective models for real-world biomedical problems.

This is not a plug-and-play ML role - we are looking for individuals who understand why models work, not just how to run them.

• Design and implement deep learning architectures, with a strong emphasis on Graph Neural Networks (GNNs) for biological and molecular data
• Develop models for molecular graphs, protein/peptide structures, interaction networks, and omics data
• Research, evaluate, and implement state-of-the-art methods (message passing, equivariant models, attention mechanisms, representation learning)
• Perform rigorous model evaluation, ablation studies, and error analysis grounded in both math and domain context
• Automate training, evaluation, and experimentation pipelines for large datasets
• Collaborate with biologists and domain experts to translate biological hypotheses into computational models
• Analyze results to extract scientifically interpretable and actionable insights

• Strong understanding of deep learning fundamentals, including:
• Linear algebra, probability, optimization
• Backpropagation, loss functions, regularization
• Bias-variance tradeoffs and generalization
• Proven experience with Graph Neural Networks, such as:
• GCN, GAT, MPNN, Graph Transformers, or related architectures
• Practical understanding of message passing, aggregation, and graph representations
• Ability to derive, reason about, and modify model formulations, not just apply libraries

• Strong proficiency in Python
• Hands-on experience with PyTorch (preferred) and/or TensorFlow
• Familiarity with ML/DL tooling for experimentation, logging, and reproducibility

• Prior experience handling biological or molecular datasets (e.g., genomics, proteomics, peptides, molecular graphs, interaction networks)
• Familiarity with bioinformatics workflows, data preprocessing, and domain constraints
• Ability to bridge biological intuition and mathematical modeling

• Experience with equivariant or physics-informed models (e.g., geometric deep learning)
• Exposure to protein language models, molecular descriptors, or structural biology data
• Experience deploying or training models on cloud platforms (AWS, GCP, Azure)
• Master's or Ph.D. in Computer Science, Bioinformatics, Computational Biology, Applied Mathematics, Physics, or related fields

• Depth over buzzwords
• Mathematical clarity over black-box usage
• Curiosity, rigor, and the ability to question assumptions
• Prior real work on GNNs, not just coursework or tutorials
• If someone cannot explain their model mathematically, they are not a fit.

Moleculyst Ventures Private Limited is a deep-tech biotechnology startup operating at the intersection of computational biology, artificial intelligence, and genomics, with a mission to enable precision and personalized healthcare.

We focus on modeling complex biological systems by uncovering interactions between genetics, molecular structure, and environmental factors. Our work spans AI-driven diagnostics, therapeutic discovery, and preventive healthcare, leveraging graph-based representations, large-scale biological datasets, and first-principles reasoning. Moleculyst aims to empower clinicians and researchers with scientifically grounded, next-generation computational tools.

This is a remote contract role for a Deep Learning Specialist with prior hands-on experience in Graph Neural Networks (GNNs) and a strong mathematical foundation.

The role involves designing, implementing, and optimizing deep learning models for large-scale biological data, including molecular, genomic, and interaction networks. You will work closely with interdisciplinary teams spanning biology, chemistry, and data science to develop theoretically sound and practically effective models for real-world biomedical problems.

This is not a plug-and-play ML role - we are looking for individuals who understand why models work, not just how to run them.

• Design and implement deep learning architectures, with a strong emphasis on Graph Neural Networks (GNNs) for biological and molecular data
• Develop models for molecular graphs, protein/peptide structures, interaction networks, and omics data
• Research, evaluate, and implement state-of-the-art methods (message passing, equivariant models, attention mechanisms, representation learning)
• Perform rigorous model evaluation, ablation studies, and error analysis grounded in both math and domain context
• Automate training, evaluation, and experimentation pipelines for large datasets
• Collaborate with biologists and domain experts to translate biological hypotheses into computational models
• Analyze results to extract scientifically interpretable and actionable insights

• Strong understanding of deep learning fundamentals, including:
• Linear algebra, probability, optimization
• Backpropagation, loss functions, regularization
• Bias-variance tradeoffs and generalization
• Proven experience with Graph Neural Networks, such as:
• GCN, GAT, MPNN, Graph Transformers, or related architectures
• Practical understanding of message passing, aggregation, and graph representations
• Ability to derive, reason about, and modify model formulations, not just apply libraries

• Strong proficiency in Python
• Hands-on experience with PyTorch (preferred) and/or TensorFlow
• Familiarity with ML/DL tooling for experimentation, logging, and reproducibility

• Prior experience handling biological or molecular datasets (e.g., genomics, proteomics, peptides, molecular graphs, interaction networks)
• Familiarity with bioinformatics workflows, data preprocessing, and domain constraints
• Ability to bridge biological intuition and mathematical modeling

• Experience with equivariant or physics-informed models (e.g., geometric deep learning)
• Exposure to protein language models, molecular descriptors, or structural biology data
• Experience deploying or training models on cloud platforms (AWS, GCP, Azure)
• Master's or Ph.D. in Computer Science, Bioinformatics, Computational Biology, Applied Mathematics, Physics, or related fields

• Depth over buzzwords
• Mathematical clarity over black-box usage
• Curiosity, rigor, and the ability to question assumptions
• Prior real work on GNNs, not just coursework or tutorials
• If someone cannot explain their model mathematically, they are not a fit.

Moleculyst Ventures Private Limited is a deep-tech biotechnology startup operating at the intersection of computational biology, artificial intelligence, and genomics, with a mission to enable precision and personalized healthcare.

We focus on modeling complex biological systems by uncovering interactions between genetics, molecular structure, and environmental factors. Our work spans AI-driven diagnostics, therapeutic discovery, and preventive healthcare, leveraging graph-based representations, large-scale biological datasets, and first-principles reasoning. Moleculyst aims to empower clinicians and researchers with scientifically grounded, next-generation computational tools.

This is a remote contract role for a Deep Learning Specialist with prior hands-on experience in Graph Neural Networks (GNNs) and a strong mathematical foundation.

The role involves designing, implementing, and optimizing deep learning models for large-scale biological data, including molecular, genomic, and interaction networks. You will work closely with interdisciplinary teams spanning biology, chemistry, and data science to develop theoretically sound and practically effective models for real-world biomedical problems.

This is not a plug-and-play ML role - we are looking for individuals who understand why models work, not just how to run them.

• Design and implement deep learning architectures, with a strong emphasis on Graph Neural Networks (GNNs) for biological and molecular data
• Develop models for molecular graphs, protein/peptide structures, interaction networks, and omics data
• Research, evaluate, and implement state-of-the-art methods (message passing, equivariant models, attention mechanisms, representation learning)
• Perform rigorous model evaluation, ablation studies, and error analysis grounded in both math and domain context
• Automate training, evaluation, and experimentation pipelines for large datasets
• Collaborate with biologists and domain experts to translate biological hypotheses into computational models
• Analyze results to extract scientifically interpretable and actionable insights

• Strong understanding of deep learning fundamentals, including:
• Linear algebra, probability, optimization
• Backpropagation, loss functions, regularization
• Bias-variance tradeoffs and generalization
• Proven experience with Graph Neural Networks, such as:
• GCN, GAT, MPNN, Graph Transformers, or related architectures
• Practical understanding of message passing, aggregation, and graph representations
• Ability to derive, reason about, and modify model formulations, not just apply libraries

• Strong proficiency in Python
• Hands-on experience with PyTorch (preferred) and/or TensorFlow
• Familiarity with ML/DL tooling for experimentation, logging, and reproducibility

• Prior experience handling biological or molecular datasets (e.g., genomics, proteomics, peptides, molecular graphs, interaction networks)
• Familiarity with bioinformatics workflows, data preprocessing, and domain constraints
• Ability to bridge biological intuition and mathematical modeling

• Experience with equivariant or physics-informed models (e.g., geometric deep learning)
• Exposure to protein language models, molecular descriptors, or structural biology data
• Experience deploying or training models on cloud platforms (AWS, GCP, Azure)
• Master's or Ph.D. in Computer Science, Bioinformatics, Computational Biology, Applied Mathematics, Physics, or related fields

• Depth over buzzwords
• Mathematical clarity over black-box usage
• Curiosity, rigor, and the ability to question assumptions
• Prior real work on GNNs, not just coursework or tutorials
• If someone cannot explain their model mathematically, they are not a fit.

Moleculyst Ventures Private Limited is a deep-tech biotechnology startup operating at the intersection of computational biology, artificial intelligence, and genomics, with a mission to enable precision and personalized healthcare.

We focus on modeling complex biological systems by uncovering interactions between genetics, molecular structure, and environmental factors. Our work spans AI-driven diagnostics, therapeutic discovery, and preventive healthcare, leveraging graph-based representations, large-scale biological datasets, and first-principles reasoning. Moleculyst aims to empower clinicians and researchers with scientifically grounded, next-generation computational tools.

This is a remote contract role for a Deep Learning Specialist with prior hands-on experience in Graph Neural Networks (GNNs) and a strong mathematical foundation.

The role involves designing, implementing, and optimizing deep learning models for large-scale biological data, including molecular, genomic, and interaction networks. You will work closely with interdisciplinary teams spanning biology, chemistry, and data science to develop theoretically sound and practically effective models for real-world biomedical problems.

This is not a plug-and-play ML role - we are looking for individuals who understand why models work, not just how to run them.

• Design and implement deep learning architectures, with a strong emphasis on Graph Neural Networks (GNNs) for biological and molecular data
• Develop models for molecular graphs, protein/peptide structures, interaction networks, and omics data
• Research, evaluate, and implement state-of-the-art methods (message passing, equivariant models, attention mechanisms, representation learning)
• Perform rigorous model evaluation, ablation studies, and error analysis grounded in both math and domain context
• Automate training, evaluation, and experimentation pipelines for large datasets
• Collaborate with biologists and domain experts to translate biological hypotheses into computational models
• Analyze results to extract scientifically interpretable and actionable insights

• Strong understanding of deep learning fundamentals, including:
• Linear algebra, probability, optimization
• Backpropagation, loss functions, regularization
• Bias-variance tradeoffs and generalization
• Proven experience with Graph Neural Networks, such as:
• GCN, GAT, MPNN, Graph Transformers, or related architectures
• Practical understanding of message passing, aggregation, and graph representations
• Ability to derive, reason about, and modify model formulations, not just apply libraries

• Strong proficiency in Python
• Hands-on experience with PyTorch (preferred) and/or TensorFlow
• Familiarity with ML/DL tooling for experimentation, logging, and reproducibility

• Prior experience handling biological or molecular datasets (e.g., genomics, proteomics, peptides, molecular graphs, interaction networks)
• Familiarity with bioinformatics workflows, data preprocessing, and domain constraints
• Ability to bridge biological intuition and mathematical modeling

• Experience with equivariant or physics-informed models (e.g., geometric deep learning)
• Exposure to protein language models, molecular descriptors, or structural biology data
• Experience deploying or training models on cloud platforms (AWS, GCP, Azure)
• Master's or Ph.D. in Computer Science, Bioinformatics, Computational Biology, Applied Mathematics, Physics, or related fields

• Depth over buzzwords
• Mathematical clarity over black-box usage
• Curiosity, rigor, and the ability to question assumptions
• Prior real work on GNNs, not just coursework or tutorials
• If someone cannot explain their model mathematically, they are not a fit.

Moleculyst Ventures Private Limited is a deep-tech biotechnology startup operating at the intersection of computational biology, artificial intelligence, and genomics, with a mission to enable precision and personalized healthcare.

We focus on modeling complex biological systems by uncovering interactions between genetics, molecular structure, and environmental factors. Our work spans AI-driven diagnostics, therapeutic discovery, and preventive healthcare, leveraging graph-based representations, large-scale biological datasets, and first-principles reasoning. Moleculyst aims to empower clinicians and researchers with scientifically grounded, next-generation computational tools.

This is a remote contract role for a Deep Learning Specialist with prior hands-on experience in Graph Neural Networks (GNNs) and a strong mathematical foundation.

The role involves designing, implementing, and optimizing deep learning models for large-scale biological data, including molecular, genomic, and interaction networks. You will work closely with interdisciplinary teams spanning biology, chemistry, and data science to develop theoretically sound and practically effective models for real-world biomedical problems.

This is not a plug-and-play ML role - we are looking for individuals who understand why models work, not just how to run them.

• Design and implement deep learning architectures, with a strong emphasis on Graph Neural Networks (GNNs) for biological and molecular data
• Develop models for molecular graphs, protein/peptide structures, interaction networks, and omics data
• Research, evaluate, and implement state-of-the-art methods (message passing, equivariant models, attention mechanisms, representation learning)
• Perform rigorous model evaluation, ablation studies, and error analysis grounded in both math and domain context
• Automate training, evaluation, and experimentation pipelines for large datasets
• Collaborate with biologists and domain experts to translate biological hypotheses into computational models
• Analyze results to extract scientifically interpretable and actionable insights

• Strong understanding of deep learning fundamentals, including:
• Linear algebra, probability, optimization
• Backpropagation, loss functions, regularization
• Bias-variance tradeoffs and generalization
• Proven experience with Graph Neural Networks, such as:
• GCN, GAT, MPNN, Graph Transformers, or related architectures
• Practical understanding of message passing, aggregation, and graph representations
• Ability to derive, reason about, and modify model formulations, not just apply libraries

• Strong proficiency in Python
• Hands-on experience with PyTorch (preferred) and/or TensorFlow
• Familiarity with ML/DL tooling for experimentation, logging, and reproducibility

• Prior experience handling biological or molecular datasets (e.g., genomics, proteomics, peptides, molecular graphs, interaction networks)
• Familiarity with bioinformatics workflows, data preprocessing, and domain constraints
• Ability to bridge biological intuition and mathematical modeling

• Experience with equivariant or physics-informed models (e.g., geometric deep learning)
• Exposure to protein language models, molecular descriptors, or structural biology data
• Experience deploying or training models on cloud platforms (AWS, GCP, Azure)
• Master's or Ph.D. in Computer Science, Bioinformatics, Computational Biology, Applied Mathematics, Physics, or related fields

• Depth over buzzwords
• Mathematical clarity over black-box usage
• Curiosity, rigor, and the ability to question assumptions
• Prior real work on GNNs, not just coursework or tutorials
• If someone cannot explain their model mathematically, they are not a fit.

Moleculyst Ventures Private Limited is a deep-tech biotechnology startup operating at the intersection of computational biology, artificial intelligence, and genomics, with a mission to enable precision and personalized healthcare.

We focus on modeling complex biological systems by uncovering interactions between genetics, molecular structure, and environmental factors. Our work spans AI-driven diagnostics, therapeutic discovery, and preventive healthcare, leveraging graph-based representations, large-scale biological datasets, and first-principles reasoning. Moleculyst aims to empower clinicians and researchers with scientifically grounded, next-generation computational tools.

This is a remote contract role for a Deep Learning Specialist with prior hands-on experience in Graph Neural Networks (GNNs) and a strong mathematical foundation.

The role involves designing, implementing, and optimizing deep learning models for large-scale biological data, including molecular, genomic, and interaction networks. You will work closely with interdisciplinary teams spanning biology, chemistry, and data science to develop theoretically sound and practically effective models for real-world biomedical problems.

This is not a plug-and-play ML role - we are looking for individuals who understand why models work, not just how to run them.

• Design and implement deep learning architectures, with a strong emphasis on Graph Neural Networks (GNNs) for biological and molecular data
• Develop models for molecular graphs, protein/peptide structures, interaction networks, and omics data
• Research, evaluate, and implement state-of-the-art methods (message passing, equivariant models, attention mechanisms, representation learning)
• Perform rigorous model evaluation, ablation studies, and error analysis grounded in both math and domain context
• Automate training, evaluation, and experimentation pipelines for large datasets
• Collaborate with biologists and domain experts to translate biological hypotheses into computational models
• Analyze results to extract scientifically interpretable and actionable insights

• Strong understanding of deep learning fundamentals, including:
• Linear algebra, probability, optimization
• Backpropagation, loss functions, regularization
• Bias-variance tradeoffs and generalization
• Proven experience with Graph Neural Networks, such as:
• GCN, GAT, MPNN, Graph Transformers, or related architectures
• Practical understanding of message passing, aggregation, and graph representations
• Ability to derive, reason about, and modify model formulations, not just apply libraries

• Strong proficiency in Python
• Hands-on experience with PyTorch (preferred) and/or TensorFlow
• Familiarity with ML/DL tooling for experimentation, logging, and reproducibility

• Prior experience handling biological or molecular datasets (e.g., genomics, proteomics, peptides, molecular graphs, interaction networks)
• Familiarity with bioinformatics workflows, data preprocessing, and domain constraints
• Ability to bridge biological intuition and mathematical modeling

• Experience with equivariant or physics-informed models (e.g., geometric deep learning)
• Exposure to protein language models, molecular descriptors, or structural biology data
• Experience deploying or training models on cloud platforms (AWS, GCP, Azure)
• Master's or Ph.D. in Computer Science, Bioinformatics, Computational Biology, Applied Mathematics, Physics, or related fields

• Depth over buzzwords
• Mathematical clarity over black-box usage
• Curiosity, rigor, and the ability to question assumptions
• Prior real work on GNNs, not just coursework or tutorials
• If someone cannot explain their model mathematically, they are not a fit.

Moleculyst Ventures Private Limited is a deep-tech biotechnology startup operating at the intersection of computational biology, artificial intelligence, and genomics, with a mission to enable precision and personalized healthcare.

We focus on modeling complex biological systems by uncovering interactions between genetics, molecular structure, and environmental factors. Our work spans AI-driven diagnostics, therapeutic discovery, and preventive healthcare, leveraging graph-based representations, large-scale biological datasets, and first-principles reasoning. Moleculyst aims to empower clinicians and researchers with scientifically grounded, next-generation computational tools.

This is a remote contract role for a Deep Learning Specialist with prior hands-on experience in Graph Neural Networks (GNNs) and a strong mathematical foundation.

The role involves designing, implementing, and optimizing deep learning models for large-scale biological data, including molecular, genomic, and interaction networks. You will work closely with interdisciplinary teams spanning biology, chemistry, and data science to develop theoretically sound and practically effective models for real-world biomedical problems.

This is not a plug-and-play ML role - we are looking for individuals who understand why models work, not just how to run them.

• Design and implement deep learning architectures, with a strong emphasis on Graph Neural Networks (GNNs) for biological and molecular data
• Develop models for molecular graphs, protein/peptide structures, interaction networks, and omics data
• Research, evaluate, and implement state-of-the-art methods (message passing, equivariant models, attention mechanisms, representation learning)
• Perform rigorous model evaluation, ablation studies, and error analysis grounded in both math and domain context
• Automate training, evaluation, and experimentation pipelines for large datasets
• Collaborate with biologists and domain experts to translate biological hypotheses into computational models
• Analyze results to extract scientifically interpretable and actionable insights

• Strong understanding of deep learning fundamentals, including:
• Linear algebra, probability, optimization
• Backpropagation, loss functions, regularization
• Bias-variance tradeoffs and generalization
• Proven experience with Graph Neural Networks, such as:
• GCN, GAT, MPNN, Graph Transformers, or related architectures
• Practical understanding of message passing, aggregation, and graph representations
• Ability to derive, reason about, and modify model formulations, not just apply libraries

• Strong proficiency in Python
• Hands-on experience with PyTorch (preferred) and/or TensorFlow
• Familiarity with ML/DL tooling for experimentation, logging, and reproducibility

• Prior experience handling biological or molecular datasets (e.g., genomics, proteomics, peptides, molecular graphs, interaction networks)
• Familiarity with bioinformatics workflows, data preprocessing, and domain constraints
• Ability to bridge biological intuition and mathematical modeling

• Experience with equivariant or physics-informed models (e.g., geometric deep learning)
• Exposure to protein language models, molecular descriptors, or structural biology data
• Experience deploying or training models on cloud platforms (AWS, GCP, Azure)
• Master's or Ph.D. in Computer Science, Bioinformatics, Computational Biology, Applied Mathematics, Physics, or related fields

• Depth over buzzwords
• Mathematical clarity over black-box usage
• Curiosity, rigor, and the ability to question assumptions
• Prior real work on GNNs, not just coursework or tutorials
• If someone cannot explain their model mathematically, they are not a fit.

Moleculyst Ventures Private Limited is a deep-tech biotechnology startup operating at the intersection of computational biology, artificial intelligence, and genomics, with a mission to enable precision and personalized healthcare.

We focus on modeling complex biological systems by uncovering interactions between genetics, molecular structure, and environmental factors. Our work spans AI-driven diagnostics, therapeutic discovery, and preventive healthcare, leveraging graph-based representations, large-scale biological datasets, and first-principles reasoning. Moleculyst aims to empower clinicians and researchers with scientifically grounded, next-generation computational tools.

This is a remote contract role for a Deep Learning Specialist with prior hands-on experience in Graph Neural Networks (GNNs) and a strong mathematical foundation.

The role involves designing, implementing, and optimizing deep learning models for large-scale biological data, including molecular, genomic, and interaction networks. You will work closely with interdisciplinary teams spanning biology, chemistry, and data science to develop theoretically sound and practically effective models for real-world biomedical problems.

This is not a plug-and-play ML role - we are looking for individuals who understand why models work, not just how to run them.

• Design and implement deep learning architectures, with a strong emphasis on Graph Neural Networks (GNNs) for biological and molecular data
• Develop models for molecular graphs, protein/peptide structures, interaction networks, and omics data
• Research, evaluate, and implement state-of-the-art methods (message passing, equivariant models, attention mechanisms, representation learning)
• Perform rigorous model evaluation, ablation studies, and error analysis grounded in both math and domain context
• Automate training, evaluation, and experimentation pipelines for large datasets
• Collaborate with biologists and domain experts to translate biological hypotheses into computational models
• Analyze results to extract scientifically interpretable and actionable insights

• Strong understanding of deep learning fundamentals, including:
• Linear algebra, probability, optimization
• Backpropagation, loss functions, regularization
• Bias-variance tradeoffs and generalization
• Proven experience with Graph Neural Networks, such as:
• GCN, GAT, MPNN, Graph Transformers, or related architectures
• Practical understanding of message passing, aggregation, and graph representations
• Ability to derive, reason about, and modify model formulations, not just apply libraries

• Strong proficiency in Python
• Hands-on experience with PyTorch (preferred) and/or TensorFlow
• Familiarity with ML/DL tooling for experimentation, logging, and reproducibility

• Prior experience handling biological or molecular datasets (e.g., genomics, proteomics, peptides, molecular graphs, interaction networks)
• Familiarity with bioinformatics workflows, data preprocessing, and domain constraints
• Ability to bridge biological intuition and mathematical modeling

• Experience with equivariant or physics-informed models (e.g., geometric deep learning)
• Exposure to protein language models, molecular descriptors, or structural biology data
• Experience deploying or training models on cloud platforms (AWS, GCP, Azure)
• Master's or Ph.D. in Computer Science, Bioinformatics, Computational Biology, Applied Mathematics, Physics, or related fields

• Depth over buzzwords
• Mathematical clarity over black-box usage
• Curiosity, rigor, and the ability to question assumptions
• Prior real work on GNNs, not just coursework or tutorials
• If someone cannot explain their model mathematically, they are not a fit.

Moleculyst Ventures Private Limited is a deep-tech biotechnology startup operating at the intersection of computational biology, artificial intelligence, and genomics, with a mission to enable precision and personalized healthcare.

We focus on modeling complex biological systems by uncovering interactions between genetics, molecular structure, and environmental factors. Our work spans AI-driven diagnostics, therapeutic discovery, and preventive healthcare, leveraging graph-based representations, large-scale biological datasets, and first-principles reasoning. Moleculyst aims to empower clinicians and researchers with scientifically grounded, next-generation computational tools.

This is a remote contract role for a Deep Learning Specialist with prior hands-on experience in Graph Neural Networks (GNNs) and a strong mathematical foundation.

The role involves designing, implementing, and optimizing deep learning models for large-scale biological data, including molecular, genomic, and interaction networks. You will work closely with interdisciplinary teams spanning biology, chemistry, and data science to develop theoretically sound and practically effective models for real-world biomedical problems.

This is not a plug-and-play ML role - we are looking for individuals who understand why models work, not just how to run them.

• Design and implement deep learning architectures, with a strong emphasis on Graph Neural Networks (GNNs) for biological and molecular data
• Develop models for molecular graphs, protein/peptide structures, interaction networks, and omics data
• Research, evaluate, and implement state-of-the-art methods (message passing, equivariant models, attention mechanisms, representation learning)
• Perform rigorous model evaluation, ablation studies, and error analysis grounded in both math and domain context
• Automate training, evaluation, and experimentation pipelines for large datasets
• Collaborate with biologists and domain experts to translate biological hypotheses into computational models
• Analyze results to extract scientifically interpretable and actionable insights

• Strong understanding of deep learning fundamentals, including:
• Linear algebra, probability, optimization
• Backpropagation, loss functions, regularization
• Bias-variance tradeoffs and generalization
• Proven experience with Graph Neural Networks, such as:
• GCN, GAT, MPNN, Graph Transformers, or related architectures
• Practical understanding of message passing, aggregation, and graph representations
• Ability to derive, reason about, and modify model formulations, not just apply libraries

• Strong proficiency in Python
• Hands-on experience with PyTorch (preferred) and/or TensorFlow
• Familiarity with ML/DL tooling for experimentation, logging, and reproducibility

• Prior experience handling biological or molecular datasets (e.g., genomics, proteomics, peptides, molecular graphs, interaction networks)
• Familiarity with bioinformatics workflows, data preprocessing, and domain constraints
• Ability to bridge biological intuition and mathematical modeling

• Experience with equivariant or physics-informed models (e.g., geometric deep learning)
• Exposure to protein language models, molecular descriptors, or structural biology data
• Experience deploying or training models on cloud platforms (AWS, GCP, Azure)
• Master's or Ph.D. in Computer Science, Bioinformatics, Computational Biology, Applied Mathematics, Physics, or related fields

• Depth over buzzwords
• Mathematical clarity over black-box usage
• Curiosity, rigor, and the ability to question assumptions
• Prior real work on GNNs, not just coursework or tutorials
• If someone cannot explain their model mathematically, they are not a fit.

Moleculyst Ventures Private Limited is a deep-tech biotechnology startup operating at the intersection of computational biology, artificial intelligence, and genomics, with a mission to enable precision and personalized healthcare.

We focus on modeling complex biological systems by uncovering interactions between genetics, molecular structure, and environmental factors. Our work spans AI-driven diagnostics, therapeutic discovery, and preventive healthcare, leveraging graph-based representations, large-scale biological datasets, and first-principles reasoning. Moleculyst aims to empower clinicians and researchers with scientifically grounded, next-generation computational tools.

This is a remote contract role for a Deep Learning Specialist with prior hands-on experience in Graph Neural Networks (GNNs) and a strong mathematical foundation.

The role involves designing, implementing, and optimizing deep learning models for large-scale biological data, including molecular, genomic, and interaction networks. You will work closely with interdisciplinary teams spanning biology, chemistry, and data science to develop theoretically sound and practically effective models for real-world biomedical problems.

This is not a plug-and-play ML role - we are looking for individuals who understand why models work, not just how to run them.

• Design and implement deep learning architectures, with a strong emphasis on Graph Neural Networks (GNNs) for biological and molecular data
• Develop models for molecular graphs, protein/peptide structures, interaction networks, and omics data
• Research, evaluate, and implement state-of-the-art methods (message passing, equivariant models, attention mechanisms, representation learning)
• Perform rigorous model evaluation, ablation studies, and error analysis grounded in both math and domain context
• Automate training, evaluation, and experimentation pipelines for large datasets
• Collaborate with biologists and domain experts to translate biological hypotheses into computational models
• Analyze results to extract scientifically interpretable and actionable insights

• Strong understanding of deep learning fundamentals, including:
• Linear algebra, probability, optimization
• Backpropagation, loss functions, regularization
• Bias-variance tradeoffs and generalization
• Proven experience with Graph Neural Networks, such as:
• GCN, GAT, MPNN, Graph Transformers, or related architectures
• Practical understanding of message passing, aggregation, and graph representations
• Ability to derive, reason about, and modify model formulations, not just apply libraries

• Strong proficiency in Python
• Hands-on experience with PyTorch (preferred) and/or TensorFlow
• Familiarity with ML/DL tooling for experimentation, logging, and reproducibility

• Prior experience handling biological or molecular datasets (e.g., genomics, proteomics, peptides, molecular graphs, interaction networks)
• Familiarity with bioinformatics workflows, data preprocessing, and domain constraints
• Ability to bridge biological intuition and mathematical modeling

• Experience with equivariant or physics-informed models (e.g., geometric deep learning)
• Exposure to protein language models, molecular descriptors, or structural biology data
• Experience deploying or training models on cloud platforms (AWS, GCP, Azure)
• Master's or Ph.D. in Computer Science, Bioinformatics, Computational Biology, Applied Mathematics, Physics, or related fields

• Depth over buzzwords
• Mathematical clarity over black-box usage
• Curiosity, rigor, and the ability to question assumptions
• Prior real work on GNNs, not just coursework or tutorials
• If someone cannot explain their model mathematically, they are not a fit.

Moleculyst Ventures Private Limited is a deep-tech biotechnology startup operating at the intersection of computational biology, artificial intelligence, and genomics, with a mission to enable precision and personalized healthcare.

We focus on modeling complex biological systems by uncovering interactions between genetics, molecular structure, and environmental factors. Our work spans AI-driven diagnostics, therapeutic discovery, and preventive healthcare, leveraging graph-based representations, large-scale biological datasets, and first-principles reasoning. Moleculyst aims to empower clinicians and researchers with scientifically grounded, next-generation computational tools.

This is a remote contract role for a Deep Learning Specialist with prior hands-on experience in Graph Neural Networks (GNNs) and a strong mathematical foundation.

The role involves designing, implementing, and optimizing deep learning models for large-scale biological data, including molecular, genomic, and interaction networks. You will work closely with interdisciplinary teams spanning biology, chemistry, and data science to develop theoretically sound and practically effective models for real-world biomedical problems.

This is not a plug-and-play ML role - we are looking for individuals who understand why models work, not just how to run them.

• Design and implement deep learning architectures, with a strong emphasis on Graph Neural Networks (GNNs) for biological and molecular data
• Develop models for molecular graphs, protein/peptide structures, interaction networks, and omics data
• Research, evaluate, and implement state-of-the-art methods (message passing, equivariant models, attention mechanisms, representation learning)
• Perform rigorous model evaluation, ablation studies, and error analysis grounded in both math and domain context
• Automate training, evaluation, and experimentation pipelines for large datasets
• Collaborate with biologists and domain experts to translate biological hypotheses into computational models
• Analyze results to extract scientifically interpretable and actionable insights

• Strong understanding of deep learning fundamentals, including:
• Linear algebra, probability, optimization
• Backpropagation, loss functions, regularization
• Bias-variance tradeoffs and generalization
• Proven experience with Graph Neural Networks, such as:
• GCN, GAT, MPNN, Graph Transformers, or related architectures
• Practical understanding of message passing, aggregation, and graph representations
• Ability to derive, reason about, and modify model formulations, not just apply libraries

• Strong proficiency in Python
• Hands-on experience with PyTorch (preferred) and/or TensorFlow
• Familiarity with ML/DL tooling for experimentation, logging, and reproducibility

• Prior experience handling biological or molecular datasets (e.g., genomics, proteomics, peptides, molecular graphs, interaction networks)
• Familiarity with bioinformatics workflows, data preprocessing, and domain constraints
• Ability to bridge biological intuition and mathematical modeling

• Experience with equivariant or physics-informed models (e.g., geometric deep learning)
• Exposure to protein language models, molecular descriptors, or structural biology data
• Experience deploying or training models on cloud platforms (AWS, GCP, Azure)
• Master's or Ph.D. in Computer Science, Bioinformatics, Computational Biology, Applied Mathematics, Physics, or related fields

• Depth over buzzwords
• Mathematical clarity over black-box usage
• Curiosity, rigor, and the ability to question assumptions
• Prior real work on GNNs, not just coursework or tutorials
• If someone cannot explain their model mathematically, they are not a fit.

Moleculyst Ventures Private Limited is a deep-tech biotechnology startup operating at the intersection of computational biology, artificial intelligence, and genomics, with a mission to enable precision and personalized healthcare.

We focus on modeling complex biological systems by uncovering interactions between genetics, molecular structure, and environmental factors. Our work spans AI-driven diagnostics, therapeutic discovery, and preventive healthcare, leveraging graph-based representations, large-scale biological datasets, and first-principles reasoning. Moleculyst aims to empower clinicians and researchers with scientifically grounded, next-generation computational tools.

This is a remote contract role for a Deep Learning Specialist with prior hands-on experience in Graph Neural Networks (GNNs) and a strong mathematical foundation.

The role involves designing, implementing, and optimizing deep learning models for large-scale biological data, including molecular, genomic, and interaction networks. You will work closely with interdisciplinary teams spanning biology, chemistry, and data science to develop theoretically sound and practically effective models for real-world biomedical problems.

This is not a plug-and-play ML role - we are looking for individuals who understand why models work, not just how to run them.

• Design and implement deep learning architectures, with a strong emphasis on Graph Neural Networks (GNNs) for biological and molecular data
• Develop models for molecular graphs, protein/peptide structures, interaction networks, and omics data
• Research, evaluate, and implement state-of-the-art methods (message passing, equivariant models, attention mechanisms, representation learning)
• Perform rigorous model evaluation, ablation studies, and error analysis grounded in both math and domain context
• Automate training, evaluation, and experimentation pipelines for large datasets
• Collaborate with biologists and domain experts to translate biological hypotheses into computational models
• Analyze results to extract scientifically interpretable and actionable insights

• Strong understanding of deep learning fundamentals, including:
• Linear algebra, probability, optimization
• Backpropagation, loss functions, regularization
• Bias-variance tradeoffs and generalization
• Proven experience with Graph Neural Networks, such as:
• GCN, GAT, MPNN, Graph Transformers, or related architectures
• Practical understanding of message passing, aggregation, and graph representations
• Ability to derive, reason about, and modify model formulations, not just apply libraries

• Strong proficiency in Python
• Hands-on experience with PyTorch (preferred) and/or TensorFlow
• Familiarity with ML/DL tooling for experimentation, logging, and reproducibility

• Prior experience handling biological or molecular datasets (e.g., genomics, proteomics, peptides, molecular graphs, interaction networks)
• Familiarity with bioinformatics workflows, data preprocessing, and domain constraints
• Ability to bridge biological intuition and mathematical modeling

• Experience with equivariant or physics-informed models (e.g., geometric deep learning)
• Exposure to protein language models, molecular descriptors, or structural biology data
• Experience deploying or training models on cloud platforms (AWS, GCP, Azure)
• Master's or Ph.D. in Computer Science, Bioinformatics, Computational Biology, Applied Mathematics, Physics, or related fields

• Depth over buzzwords
• Mathematical clarity over black-box usage
• Curiosity, rigor, and the ability to question assumptions
• Prior real work on GNNs, not just coursework or tutorials
• If someone cannot explain their model mathematically, they are not a fit.

Moleculyst Ventures Private Limited is a deep-tech biotechnology startup operating at the intersection of computational biology, artificial intelligence, and genomics, with a mission to enable precision and personalized healthcare.

We focus on modeling complex biological systems by uncovering interactions between genetics, molecular structure, and environmental factors. Our work spans AI-driven diagnostics, therapeutic discovery, and preventive healthcare, leveraging graph-based representations, large-scale biological datasets, and first-principles reasoning. Moleculyst aims to empower clinicians and researchers with scientifically grounded, next-generation computational tools.

This is a remote contract role for a Deep Learning Specialist with prior hands-on experience in Graph Neural Networks (GNNs) and a strong mathematical foundation.

The role involves designing, implementing, and optimizing deep learning models for large-scale biological data, including molecular, genomic, and interaction networks. You will work closely with interdisciplinary teams spanning biology, chemistry, and data science to develop theoretically sound and practically effective models for real-world biomedical problems.

This is not a plug-and-play ML role - we are looking for individuals who understand why models work, not just how to run them.

• Design and implement deep learning architectures, with a strong emphasis on Graph Neural Networks (GNNs) for biological and molecular data
• Develop models for molecular graphs, protein/peptide structures, interaction networks, and omics data
• Research, evaluate, and implement state-of-the-art methods (message passing, equivariant models, attention mechanisms, representation learning)
• Perform rigorous model evaluation, ablation studies, and error analysis grounded in both math and domain context
• Automate training, evaluation, and experimentation pipelines for large datasets
• Collaborate with biologists and domain experts to translate biological hypotheses into computational models
• Analyze results to extract scientifically interpretable and actionable insights

• Strong understanding of deep learning fundamentals, including:
• Linear algebra, probability, optimization
• Backpropagation, loss functions, regularization
• Bias-variance tradeoffs and generalization
• Proven experience with Graph Neural Networks, such as:
• GCN, GAT, MPNN, Graph Transformers, or related architectures
• Practical understanding of message passing, aggregation, and graph representations
• Ability to derive, reason about, and modify model formulations, not just apply libraries

• Strong proficiency in Python
• Hands-on experience with PyTorch (preferred) and/or TensorFlow
• Familiarity with ML/DL tooling for experimentation, logging, and reproducibility

• Prior experience handling biological or molecular datasets (e.g., genomics, proteomics, peptides, molecular graphs, interaction networks)
• Familiarity with bioinformatics workflows, data preprocessing, and domain constraints
• Ability to bridge biological intuition and mathematical modeling

• Experience with equivariant or physics-informed models (e.g., geometric deep learning)
• Exposure to protein language models, molecular descriptors, or structural biology data
• Experience deploying or training models on cloud platforms (AWS, GCP, Azure)
• Master's or Ph.D. in Computer Science, Bioinformatics, Computational Biology, Applied Mathematics, Physics, or related fields

• Depth over buzzwords
• Mathematical clarity over black-box usage
• Curiosity, rigor, and the ability to question assumptions
• Prior real work on GNNs, not just coursework or tutorials
• If someone cannot explain their model mathematically, they are not a fit.

Moleculyst Ventures Private Limited is a deep-tech biotechnology startup operating at the intersection of computational biology, artificial intelligence, and genomics, with a mission to enable precision and personalized healthcare.

We focus on modeling complex biological systems by uncovering interactions between genetics, molecular structure, and environmental factors. Our work spans AI-driven diagnostics, therapeutic discovery, and preventive healthcare, leveraging graph-based representations, large-scale biological datasets, and first-principles reasoning. Moleculyst aims to empower clinicians and researchers with scientifically grounded, next-generation computational tools.

This is a remote contract role for a Deep Learning Specialist with prior hands-on experience in Graph Neural Networks (GNNs) and a strong mathematical foundation.

The role involves designing, implementing, and optimizing deep learning models for large-scale biological data, including molecular, genomic, and interaction networks. You will work closely with interdisciplinary teams spanning biology, chemistry, and data science to develop theoretically sound and practically effective models for real-world biomedical problems.

This is not a plug-and-play ML role - we are looking for individuals who understand why models work, not just how to run them.

• Design and implement deep learning architectures, with a strong emphasis on Graph Neural Networks (GNNs) for biological and molecular data
• Develop models for molecular graphs, protein/peptide structures, interaction networks, and omics data
• Research, evaluate, and implement state-of-the-art methods (message passing, equivariant models, attention mechanisms, representation learning)
• Perform rigorous model evaluation, ablation studies, and error analysis grounded in both math and domain context
• Automate training, evaluation, and experimentation pipelines for large datasets
• Collaborate with biologists and domain experts to translate biological hypotheses into computational models
• Analyze results to extract scientifically interpretable and actionable insights

• Strong understanding of deep learning fundamentals, including:
• Linear algebra, probability, optimization
• Backpropagation, loss functions, regularization
• Bias-variance tradeoffs and generalization
• Proven experience with Graph Neural Networks, such as:
• GCN, GAT, MPNN, Graph Transformers, or related architectures
• Practical understanding of message passing, aggregation, and graph representations
• Ability to derive, reason about, and modify model formulations, not just apply libraries

• Strong proficiency in Python
• Hands-on experience with PyTorch (preferred) and/or TensorFlow
• Familiarity with ML/DL tooling for experimentation, logging, and reproducibility

• Prior experience handling biological or molecular datasets (e.g., genomics, proteomics, peptides, molecular graphs, interaction networks)
• Familiarity with bioinformatics workflows, data preprocessing, and domain constraints
• Ability to bridge biological intuition and mathematical modeling

• Experience with equivariant or physics-informed models (e.g., geometric deep learning)
• Exposure to protein language models, molecular descriptors, or structural biology data
• Experience deploying or training models on cloud platforms (AWS, GCP, Azure)
• Master's or Ph.D. in Computer Science, Bioinformatics, Computational Biology, Applied Mathematics, Physics, or related fields

• Depth over buzzwords
• Mathematical clarity over black-box usage
• Curiosity, rigor, and the ability to question assumptions
• Prior real work on GNNs, not just coursework or tutorials
• If someone cannot explain their model mathematically, they are not a fit.

Moleculyst Ventures Private Limited is a deep-tech biotechnology startup operating at the intersection of computational biology, artificial intelligence, and genomics, with a mission to enable precision and personalized healthcare.

We focus on modeling complex biological systems by uncovering interactions between genetics, molecular structure, and environmental factors. Our work spans AI-driven diagnostics, therapeutic discovery, and preventive healthcare, leveraging graph-based representations, large-scale biological datasets, and first-principles reasoning. Moleculyst aims to empower clinicians and researchers with scientifically grounded, next-generation computational tools.

This is a remote contract role for a Deep Learning Specialist with prior hands-on experience in Graph Neural Networks (GNNs) and a strong mathematical foundation.

The role involves designing, implementing, and optimizing deep learning models for large-scale biological data, including molecular, genomic, and interaction networks. You will work closely with interdisciplinary teams spanning biology, chemistry, and data science to develop theoretically sound and practically effective models for real-world biomedical problems.

This is not a plug-and-play ML role - we are looking for individuals who understand why models work, not just how to run them.

• Design and implement deep learning architectures, with a strong emphasis on Graph Neural Networks (GNNs) for biological and molecular data
• Develop models for molecular graphs, protein/peptide structures, interaction networks, and omics data
• Research, evaluate, and implement state-of-the-art methods (message passing, equivariant models, attention mechanisms, representation learning)
• Perform rigorous model evaluation, ablation studies, and error analysis grounded in both math and domain context
• Automate training, evaluation, and experimentation pipelines for large datasets
• Collaborate with biologists and domain experts to translate biological hypotheses into computational models
• Analyze results to extract scientifically interpretable and actionable insights

• Strong understanding of deep learning fundamentals, including:
• Linear algebra, probability, optimization
• Backpropagation, loss functions, regularization
• Bias-variance tradeoffs and generalization
• Proven experience with Graph Neural Networks, such as:
• GCN, GAT, MPNN, Graph Transformers, or related architectures
• Practical understanding of message passing, aggregation, and graph representations
• Ability to derive, reason about, and modify model formulations, not just apply libraries

• Strong proficiency in Python
• Hands-on experience with PyTorch (preferred) and/or TensorFlow
• Familiarity with ML/DL tooling for experimentation, logging, and reproducibility

• Prior experience handling biological or molecular datasets (e.g., genomics, proteomics, peptides, molecular graphs, interaction networks)
• Familiarity with bioinformatics workflows, data preprocessing, and domain constraints
• Ability to bridge biological intuition and mathematical modeling

• Experience with equivariant or physics-informed models (e.g., geometric deep learning)
• Exposure to protein language models, molecular descriptors, or structural biology data
• Experience deploying or training models on cloud platforms (AWS, GCP, Azure)
• Master's or Ph.D. in Computer Science, Bioinformatics, Computational Biology, Applied Mathematics, Physics, or related fields

• Depth over buzzwords
• Mathematical clarity over black-box usage
• Curiosity, rigor, and the ability to question assumptions
• Prior real work on GNNs, not just coursework or tutorials
• If someone cannot explain their model mathematically, they are not a fit.

Moleculyst Ventures Private Limited is a deep-tech biotechnology startup operating at the intersection of computational biology, artificial intelligence, and genomics, with a mission to enable precision and personalized healthcare.

We focus on modeling complex biological systems by uncovering interactions between genetics, molecular structure, and environmental factors. Our work spans AI-driven diagnostics, therapeutic discovery, and preventive healthcare, leveraging graph-based representations, large-scale biological datasets, and first-principles reasoning. Moleculyst aims to empower clinicians and researchers with scientifically grounded, next-generation computational tools.

This is a remote contract role for a Deep Learning Specialist with prior hands-on experience in Graph Neural Networks (GNNs) and a strong mathematical foundation.

The role involves designing, implementing, and optimizing deep learning models for large-scale biological data, including molecular, genomic, and interaction networks. You will work closely with interdisciplinary teams spanning biology, chemistry, and data science to develop theoretically sound and practically effective models for real-world biomedical problems.

This is not a plug-and-play ML role - we are looking for individuals who understand why models work, not just how to run them.

• Design and implement deep learning architectures, with a strong emphasis on Graph Neural Networks (GNNs) for biological and molecular data
• Develop models for molecular graphs, protein/peptide structures, interaction networks, and omics data
• Research, evaluate, and implement state-of-the-art methods (message passing, equivariant models, attention mechanisms, representation learning)
• Perform rigorous model evaluation, ablation studies, and error analysis grounded in both math and domain context
• Automate training, evaluation, and experimentation pipelines for large datasets
• Collaborate with biologists and domain experts to translate biological hypotheses into computational models
• Analyze results to extract scientifically interpretable and actionable insights

• Strong understanding of deep learning fundamentals, including:
• Linear algebra, probability, optimization
• Backpropagation, loss functions, regularization
• Bias-variance tradeoffs and generalization
• Proven experience with Graph Neural Networks, such as:
• GCN, GAT, MPNN, Graph Transformers, or related architectures
• Practical understanding of message passing, aggregation, and graph representations
• Ability to derive, reason about, and modify model formulations, not just apply libraries

• Strong proficiency in Python
• Hands-on experience with PyTorch (preferred) and/or TensorFlow
• Familiarity with ML/DL tooling for experimentation, logging, and reproducibility

• Prior experience handling biological or molecular datasets (e.g., genomics, proteomics, peptides, molecular graphs, interaction networks)
• Familiarity with bioinformatics workflows, data preprocessing, and domain constraints
• Ability to bridge biological intuition and mathematical modeling

• Experience with equivariant or physics-informed models (e.g., geometric deep learning)
• Exposure to protein language models, molecular descriptors, or structural biology data
• Experience deploying or training models on cloud platforms (AWS, GCP, Azure)
• Master's or Ph.D. in Computer Science, Bioinformatics, Computational Biology, Applied Mathematics, Physics, or related fields

• Depth over buzzwords
• Mathematical clarity over black-box usage
• Curiosity, rigor, and the ability to question assumptions
• Prior real work on GNNs, not just coursework or tutorials
• If someone cannot explain their model mathematically, they are not a fit.

Moleculyst Ventures Private Limited is a deep-tech biotechnology startup operating at the intersection of computational biology, artificial intelligence, and genomics, with a mission to enable precision and personalized healthcare.

We focus on modeling complex biological systems by uncovering interactions between genetics, molecular structure, and environmental factors. Our work spans AI-driven diagnostics, therapeutic discovery, and preventive healthcare, leveraging graph-based representations, large-scale biological datasets, and first-principles reasoning. Moleculyst aims to empower clinicians and researchers with scientifically grounded, next-generation computational tools.

This is a remote contract role for a Deep Learning Specialist with prior hands-on experience in Graph Neural Networks (GNNs) and a strong mathematical foundation.

The role involves designing, implementing, and optimizing deep learning models for large-scale biological data, including molecular, genomic, and interaction networks. You will work closely with interdisciplinary teams spanning biology, chemistry, and data science to develop theoretically sound and practically effective models for real-world biomedical problems.

This is not a plug-and-play ML role - we are looking for individuals who understand why models work, not just how to run them.

• Design and implement deep learning architectures, with a strong emphasis on Graph Neural Networks (GNNs) for biological and molecular data
• Develop models for molecular graphs, protein/peptide structures, interaction networks, and omics data
• Research, evaluate, and implement state-of-the-art methods (message passing, equivariant models, attention mechanisms, representation learning)
• Perform rigorous model evaluation, ablation studies, and error analysis grounded in both math and domain context
• Automate training, evaluation, and experimentation pipelines for large datasets
• Collaborate with biologists and domain experts to translate biological hypotheses into computational models
• Analyze results to extract scientifically interpretable and actionable insights

• Strong understanding of deep learning fundamentals, including:
• Linear algebra, probability, optimization
• Backpropagation, loss functions, regularization
• Bias-variance tradeoffs and generalization
• Proven experience with Graph Neural Networks, such as:
• GCN, GAT, MPNN, Graph Transformers, or related architectures
• Practical understanding of message passing, aggregation, and graph representations
• Ability to derive, reason about, and modify model formulations, not just apply libraries

• Strong proficiency in Python
• Hands-on experience with PyTorch (preferred) and/or TensorFlow
• Familiarity with ML/DL tooling for experimentation, logging, and reproducibility

• Prior experience handling biological or molecular datasets (e.g., genomics, proteomics, peptides, molecular graphs, interaction networks)
• Familiarity with bioinformatics workflows, data preprocessing, and domain constraints
• Ability to bridge biological intuition and mathematical modeling

• Experience with equivariant or physics-informed models (e.g., geometric deep learning)
• Exposure to protein language models, molecular descriptors, or structural biology data
• Experience deploying or training models on cloud platforms (AWS, GCP, Azure)
• Master's or Ph.D. in Computer Science, Bioinformatics, Computational Biology, Applied Mathematics, Physics, or related fields

• Depth over buzzwords
• Mathematical clarity over black-box usage
• Curiosity, rigor, and the ability to question assumptions
• Prior real work on GNNs, not just coursework or tutorials
• If someone cannot explain their model mathematically, they are not a fit.

Moleculyst Ventures Private Limited is a deep-tech biotechnology startup operating at the intersection of computational biology, artificial intelligence, and genomics, with a mission to enable precision and personalized healthcare.

We focus on modeling complex biological systems by uncovering interactions between genetics, molecular structure, and environmental factors. Our work spans AI-driven diagnostics, therapeutic discovery, and preventive healthcare, leveraging graph-based representations, large-scale biological datasets, and first-principles reasoning. Moleculyst aims to empower clinicians and researchers with scientifically grounded, next-generation computational tools.

This is a remote contract role for a Deep Learning Specialist with prior hands-on experience in Graph Neural Networks (GNNs) and a strong mathematical foundation.

The role involves designing, implementing, and optimizing deep learning models for large-scale biological data, including molecular, genomic, and interaction networks. You will work closely with interdisciplinary teams spanning biology, chemistry, and data science to develop theoretically sound and practically effective models for real-world biomedical problems.

This is not a plug-and-play ML role - we are looking for individuals who understand why models work, not just how to run them.

• Design and implement deep learning architectures, with a strong emphasis on Graph Neural Networks (GNNs) for biological and molecular data
• Develop models for molecular graphs, protein/peptide structures, interaction networks, and omics data
• Research, evaluate, and implement state-of-the-art methods (message passing, equivariant models, attention mechanisms, representation learning)
• Perform rigorous model evaluation, ablation studies, and error analysis grounded in both math and domain context
• Automate training, evaluation, and experimentation pipelines for large datasets
• Collaborate with biologists and domain experts to translate biological hypotheses into computational models
• Analyze results to extract scientifically interpretable and actionable insights

• Strong understanding of deep learning fundamentals, including:
• Linear algebra, probability, optimization
• Backpropagation, loss functions, regularization
• Bias-variance tradeoffs and generalization
• Proven experience with Graph Neural Networks, such as:
• GCN, GAT, MPNN, Graph Transformers, or related architectures
• Practical understanding of message passing, aggregation, and graph representations
• Ability to derive, reason about, and modify model formulations, not just apply libraries

• Strong proficiency in Python
• Hands-on experience with PyTorch (preferred) and/or TensorFlow
• Familiarity with ML/DL tooling for experimentation, logging, and reproducibility

• Prior experience handling biological or molecular datasets (e.g., genomics, proteomics, peptides, molecular graphs, interaction networks)
• Familiarity with bioinformatics workflows, data preprocessing, and domain constraints
• Ability to bridge biological intuition and mathematical modeling

• Experience with equivariant or physics-informed models (e.g., geometric deep learning)
• Exposure to protein language models, molecular descriptors, or structural biology data
• Experience deploying or training models on cloud platforms (AWS, GCP, Azure)
• Master's or Ph.D. in Computer Science, Bioinformatics, Computational Biology, Applied Mathematics, Physics, or related fields

• Depth over buzzwords
• Mathematical clarity over black-box usage
• Curiosity, rigor, and the ability to question assumptions
• Prior real work on GNNs, not just coursework or tutorials
• If someone cannot explain their model mathematically, they are not a fit.

Moleculyst Ventures Private Limited is a deep-tech biotechnology startup operating at the intersection of computational biology, artificial intelligence, and genomics, with a mission to enable precision and personalized healthcare.

We focus on modeling complex biological systems by uncovering interactions between genetics, molecular structure, and environmental factors. Our work spans AI-driven diagnostics, therapeutic discovery, and preventive healthcare, leveraging graph-based representations, large-scale biological datasets, and first-principles reasoning. Moleculyst aims to empower clinicians and researchers with scientifically grounded, next-generation computational tools.

This is a remote contract role for a Deep Learning Specialist with prior hands-on experience in Graph Neural Networks (GNNs) and a strong mathematical foundation.

The role involves designing, implementing, and optimizing deep learning models for large-scale biological data, including molecular, genomic, and interaction networks. You will work closely with interdisciplinary teams spanning biology, chemistry, and data science to develop theoretically sound and practically effective models for real-world biomedical problems.

This is not a plug-and-play ML role - we are looking for individuals who understand why models work, not just how to run them.

• Design and implement deep learning architectures, with a strong emphasis on Graph Neural Networks (GNNs) for biological and molecular data
• Develop models for molecular graphs, protein/peptide structures, interaction networks, and omics data
• Research, evaluate, and implement state-of-the-art methods (message passing, equivariant models, attention mechanisms, representation learning)
• Perform rigorous model evaluation, ablation studies, and error analysis grounded in both math and domain context
• Automate training, evaluation, and experimentation pipelines for large datasets
• Collaborate with biologists and domain experts to translate biological hypotheses into computational models
• Analyze results to extract scientifically interpretable and actionable insights

• Strong understanding of deep learning fundamentals, including:
• Linear algebra, probability, optimization
• Backpropagation, loss functions, regularization
• Bias-variance tradeoffs and generalization
• Proven experience with Graph Neural Networks, such as:
• GCN, GAT, MPNN, Graph Transformers, or related architectures
• Practical understanding of message passing, aggregation, and graph representations
• Ability to derive, reason about, and modify model formulations, not just apply libraries

• Strong proficiency in Python
• Hands-on experience with PyTorch (preferred) and/or TensorFlow
• Familiarity with ML/DL tooling for experimentation, logging, and reproducibility

• Prior experience handling biological or molecular datasets (e.g., genomics, proteomics, peptides, molecular graphs, interaction networks)
• Familiarity with bioinformatics workflows, data preprocessing, and domain constraints
• Ability to bridge biological intuition and mathematical modeling

• Experience with equivariant or physics-informed models (e.g., geometric deep learning)
• Exposure to protein language models, molecular descriptors, or structural biology data
• Experience deploying or training models on cloud platforms (AWS, GCP, Azure)
• Master's or Ph.D. in Computer Science, Bioinformatics, Computational Biology, Applied Mathematics, Physics, or related fields

• Depth over buzzwords
• Mathematical clarity over black-box usage
• Curiosity, rigor, and the ability to question assumptions
• Prior real work on GNNs, not just coursework or tutorials
• If someone cannot explain their model mathematically, they are not a fit.

Moleculyst Ventures Private Limited is a deep-tech biotechnology startup operating at the intersection of computational biology, artificial intelligence, and genomics, with a mission to enable precision and personalized healthcare.

We focus on modeling complex biological systems by uncovering interactions between genetics, molecular structure, and environmental factors. Our work spans AI-driven diagnostics, therapeutic discovery, and preventive healthcare, leveraging graph-based representations, large-scale biological datasets, and first-principles reasoning. Moleculyst aims to empower clinicians and researchers with scientifically grounded, next-generation computational tools.

This is a remote contract role for a Deep Learning Specialist with prior hands-on experience in Graph Neural Networks (GNNs) and a strong mathematical foundation.

The role involves designing, implementing, and optimizing deep learning models for large-scale biological data, including molecular, genomic, and interaction networks. You will work closely with interdisciplinary teams spanning biology, chemistry, and data science to develop theoretically sound and practically effective models for real-world biomedical problems.

This is not a plug-and-play ML role - we are looking for individuals who understand why models work, not just how to run them.

• Design and implement deep learning architectures, with a strong emphasis on Graph Neural Networks (GNNs) for biological and molecular data
• Develop models for molecular graphs, protein/peptide structures, interaction networks, and omics data
• Research, evaluate, and implement state-of-the-art methods (message passing, equivariant models, attention mechanisms, representation learning)
• Perform rigorous model evaluation, ablation studies, and error analysis grounded in both math and domain context
• Automate training, evaluation, and experimentation pipelines for large datasets
• Collaborate with biologists and domain experts to translate biological hypotheses into computational models
• Analyze results to extract scientifically interpretable and actionable insights

• Strong understanding of deep learning fundamentals, including:
• Linear algebra, probability, optimization
• Backpropagation, loss functions, regularization
• Bias-variance tradeoffs and generalization
• Proven experience with Graph Neural Networks, such as:
• GCN, GAT, MPNN, Graph Transformers, or related architectures
• Practical understanding of message passing, aggregation, and graph representations
• Ability to derive, reason about, and modify model formulations, not just apply libraries

• Strong proficiency in Python
• Hands-on experience with PyTorch (preferred) and/or TensorFlow
• Familiarity with ML/DL tooling for experimentation, logging, and reproducibility

• Prior experience handling biological or molecular datasets (e.g., genomics, proteomics, peptides, molecular graphs, interaction networks)
• Familiarity with bioinformatics workflows, data preprocessing, and domain constraints
• Ability to bridge biological intuition and mathematical modeling

• Experience with equivariant or physics-informed models (e.g., geometric deep learning)
• Exposure to protein language models, molecular descriptors, or structural biology data
• Experience deploying or training models on cloud platforms (AWS, GCP, Azure)
• Master's or Ph.D. in Computer Science, Bioinformatics, Computational Biology, Applied Mathematics, Physics, or related fields

• Depth over buzzwords
• Mathematical clarity over black-box usage
• Curiosity, rigor, and the ability to question assumptions
• Prior real work on GNNs, not just coursework or tutorials
• If someone cannot explain their model mathematically, they are not a fit.

Moleculyst Ventures Private Limited is a deep-tech biotechnology startup operating at the intersection of computational biology, artificial intelligence, and genomics, with a mission to enable precision and personalized healthcare.

We focus on modeling complex biological systems by uncovering interactions between genetics, molecular structure, and environmental factors. Our work spans AI-driven diagnostics, therapeutic discovery, and preventive healthcare, leveraging graph-based representations, large-scale biological datasets, and first-principles reasoning. Moleculyst aims to empower clinicians and researchers with scientifically grounded, next-generation computational tools.

This is a remote contract role for a Deep Learning Specialist with prior hands-on experience in Graph Neural Networks (GNNs) and a strong mathematical foundation.

The role involves designing, implementing, and optimizing deep learning models for large-scale biological data, including molecular, genomic, and interaction networks. You will work closely with interdisciplinary teams spanning biology, chemistry, and data science to develop theoretically sound and practically effective models for real-world biomedical problems.

This is not a plug-and-play ML role - we are looking for individuals who understand why models work, not just how to run them.

• Design and implement deep learning architectures, with a strong emphasis on Graph Neural Networks (GNNs) for biological and molecular data
• Develop models for molecular graphs, protein/peptide structures, interaction networks, and omics data
• Research, evaluate, and implement state-of-the-art methods (message passing, equivariant models, attention mechanisms, representation learning)
• Perform rigorous model evaluation, ablation studies, and error analysis grounded in both math and domain context
• Automate training, evaluation, and experimentation pipelines for large datasets
• Collaborate with biologists and domain experts to translate biological hypotheses into computational models
• Analyze results to extract scientifically interpretable and actionable insights

• Strong understanding of deep learning fundamentals, including:
• Linear algebra, probability, optimization
• Backpropagation, loss functions, regularization
• Bias-variance tradeoffs and generalization
• Proven experience with Graph Neural Networks, such as:
• GCN, GAT, MPNN, Graph Transformers, or related architectures
• Practical understanding of message passing, aggregation, and graph representations
• Ability to derive, reason about, and modify model formulations, not just apply libraries

• Strong proficiency in Python
• Hands-on experience with PyTorch (preferred) and/or TensorFlow
• Familiarity with ML/DL tooling for experimentation, logging, and reproducibility

• Prior experience handling biological or molecular datasets (e.g., genomics, proteomics, peptides, molecular graphs, interaction networks)
• Familiarity with bioinformatics workflows, data preprocessing, and domain constraints
• Ability to bridge biological intuition and mathematical modeling

• Experience with equivariant or physics-informed models (e.g., geometric deep learning)
• Exposure to protein language models, molecular descriptors, or structural biology data
• Experience deploying or training models on cloud platforms (AWS, GCP, Azure)
• Master's or Ph.D. in Computer Science, Bioinformatics, Computational Biology, Applied Mathematics, Physics, or related fields

• Depth over buzzwords
• Mathematical clarity over black-box usage
• Curiosity, rigor, and the ability to question assumptions
• Prior real work on GNNs, not just coursework or tutorials
• If someone cannot explain their model mathematically, they are not a fit.

Moleculyst Ventures Private Limited is a deep-tech biotechnology startup operating at the intersection of computational biology, artificial intelligence, and genomics, with a mission to enable precision and personalized healthcare.

We focus on modeling complex biological systems by uncovering interactions between genetics, molecular structure, and environmental factors. Our work spans AI-driven diagnostics, therapeutic discovery, and preventive healthcare, leveraging graph-based representations, large-scale biological datasets, and first-principles reasoning. Moleculyst aims to empower clinicians and researchers with scientifically grounded, next-generation computational tools.

This is a remote contract role for a Deep Learning Specialist with prior hands-on experience in Graph Neural Networks (GNNs) and a strong mathematical foundation.

The role involves designing, implementing, and optimizing deep learning models for large-scale biological data, including molecular, genomic, and interaction networks. You will work closely with interdisciplinary teams spanning biology, chemistry, and data science to develop theoretically sound and practically effective models for real-world biomedical problems.

This is not a plug-and-play ML role - we are looking for individuals who understand why models work, not just how to run them.

• Design and implement deep learning architectures, with a strong emphasis on Graph Neural Networks (GNNs) for biological and molecular data
• Develop models for molecular graphs, protein/peptide structures, interaction networks, and omics data
• Research, evaluate, and implement state-of-the-art methods (message passing, equivariant models, attention mechanisms, representation learning)
• Perform rigorous model evaluation, ablation studies, and error analysis grounded in both math and domain context
• Automate training, evaluation, and experimentation pipelines for large datasets
• Collaborate with biologists and domain experts to translate biological hypotheses into computational models
• Analyze results to extract scientifically interpretable and actionable insights

• Strong understanding of deep learning fundamentals, including:
• Linear algebra, probability, optimization
• Backpropagation, loss functions, regularization
• Bias-variance tradeoffs and generalization
• Proven experience with Graph Neural Networks, such as:
• GCN, GAT, MPNN, Graph Transformers, or related architectures
• Practical understanding of message passing, aggregation, and graph representations
• Ability to derive, reason about, and modify model formulations, not just apply libraries

• Strong proficiency in Python
• Hands-on experience with PyTorch (preferred) and/or TensorFlow
• Familiarity with ML/DL tooling for experimentation, logging, and reproducibility

• Prior experience handling biological or molecular datasets (e.g., genomics, proteomics, peptides, molecular graphs, interaction networks)
• Familiarity with bioinformatics workflows, data preprocessing, and domain constraints
• Ability to bridge biological intuition and mathematical modeling

• Experience with equivariant or physics-informed models (e.g., geometric deep learning)
• Exposure to protein language models, molecular descriptors, or structural biology data
• Experience deploying or training models on cloud platforms (AWS, GCP, Azure)
• Master's or Ph.D. in Computer Science, Bioinformatics, Computational Biology, Applied Mathematics, Physics, or related fields

• Depth over buzzwords
• Mathematical clarity over black-box usage
• Curiosity, rigor, and the ability to question assumptions
• Prior real work on GNNs, not just coursework or tutorials
• If someone cannot explain their model mathematically, they are not a fit.

Moleculyst Ventures Private Limited is a deep-tech biotechnology startup operating at the intersection of computational biology, artificial intelligence, and genomics, with a mission to enable precision and personalized healthcare.

We focus on modeling complex biological systems by uncovering interactions between genetics, molecular structure, and environmental factors. Our work spans AI-driven diagnostics, therapeutic discovery, and preventive healthcare, leveraging graph-based representations, large-scale biological datasets, and first-principles reasoning. Moleculyst aims to empower clinicians and researchers with scientifically grounded, next-generation computational tools.

This is a remote contract role for a Deep Learning Specialist with prior hands-on experience in Graph Neural Networks (GNNs) and a strong mathematical foundation.

The role involves designing, implementing, and optimizing deep learning models for large-scale biological data, including molecular, genomic, and interaction networks. You will work closely with interdisciplinary teams spanning biology, chemistry, and data science to develop theoretically sound and practically effective models for real-world biomedical problems.

This is not a plug-and-play ML role - we are looking for individuals who understand why models work, not just how to run them.

• Design and implement deep learning architectures, with a strong emphasis on Graph Neural Networks (GNNs) for biological and molecular data
• Develop models for molecular graphs, protein/peptide structures, interaction networks, and omics data
• Research, evaluate, and implement state-of-the-art methods (message passing, equivariant models, attention mechanisms, representation learning)
• Perform rigorous model evaluation, ablation studies, and error analysis grounded in both math and domain context
• Automate training, evaluation, and experimentation pipelines for large datasets
• Collaborate with biologists and domain experts to translate biological hypotheses into computational models
• Analyze results to extract scientifically interpretable and actionable insights

• Strong understanding of deep learning fundamentals, including:
• Linear algebra, probability, optimization
• Backpropagation, loss functions, regularization
• Bias-variance tradeoffs and generalization
• Proven experience with Graph Neural Networks, such as:
• GCN, GAT, MPNN, Graph Transformers, or related architectures
• Practical understanding of message passing, aggregation, and graph representations
• Ability to derive, reason about, and modify model formulations, not just apply libraries

• Strong proficiency in Python
• Hands-on experience with PyTorch (preferred) and/or TensorFlow
• Familiarity with ML/DL tooling for experimentation, logging, and reproducibility

• Prior experience handling biological or molecular datasets (e.g., genomics, proteomics, peptides, molecular graphs, interaction networks)
• Familiarity with bioinformatics workflows, data preprocessing, and domain constraints
• Ability to bridge biological intuition and mathematical modeling

• Experience with equivariant or physics-informed models (e.g., geometric deep learning)
• Exposure to protein language models, molecular descriptors, or structural biology data
• Experience deploying or training models on cloud platforms (AWS, GCP, Azure)
• Master's or Ph.D. in Computer Science, Bioinformatics, Computational Biology, Applied Mathematics, Physics, or related fields

• Depth over buzzwords
• Mathematical clarity over black-box usage
• Curiosity, rigor, and the ability to question assumptions
• Prior real work on GNNs, not just coursework or tutorials
• If someone cannot explain their model mathematically, they are not a fit.

Moleculyst Ventures Private Limited is a deep-tech biotechnology startup operating at the intersection of computational biology, artificial intelligence, and genomics, with a mission to enable precision and personalized healthcare.

We focus on modeling complex biological systems by uncovering interactions between genetics, molecular structure, and environmental factors. Our work spans AI-driven diagnostics, therapeutic discovery, and preventive healthcare, leveraging graph-based representations, large-scale biological datasets, and first-principles reasoning. Moleculyst aims to empower clinicians and researchers with scientifically grounded, next-generation computational tools.

This is a remote contract role for a Deep Learning Specialist with prior hands-on experience in Graph Neural Networks (GNNs) and a strong mathematical foundation.

The role involves designing, implementing, and optimizing deep learning models for large-scale biological data, including molecular, genomic, and interaction networks. You will work closely with interdisciplinary teams spanning biology, chemistry, and data science to develop theoretically sound and practically effective models for real-world biomedical problems.

This is not a plug-and-play ML role - we are looking for individuals who understand why models work, not just how to run them.

• Design and implement deep learning architectures, with a strong emphasis on Graph Neural Networks (GNNs) for biological and molecular data
• Develop models for molecular graphs, protein/peptide structures, interaction networks, and omics data
• Research, evaluate, and implement state-of-the-art methods (message passing, equivariant models, attention mechanisms, representation learning)
• Perform rigorous model evaluation, ablation studies, and error analysis grounded in both math and domain context
• Automate training, evaluation, and experimentation pipelines for large datasets
• Collaborate with biologists and domain experts to translate biological hypotheses into computational models
• Analyze results to extract scientifically interpretable and actionable insights

• Strong understanding of deep learning fundamentals, including:
• Linear algebra, probability, optimization
• Backpropagation, loss functions, regularization
• Bias-variance tradeoffs and generalization
• Proven experience with Graph Neural Networks, such as:
• GCN, GAT, MPNN, Graph Transformers, or related architectures
• Practical understanding of message passing, aggregation, and graph representations
• Ability to derive, reason about, and modify model formulations, not just apply libraries

• Strong proficiency in Python
• Hands-on experience with PyTorch (preferred) and/or TensorFlow
• Familiarity with ML/DL tooling for experimentation, logging, and reproducibility

• Prior experience handling biological or molecular datasets (e.g., genomics, proteomics, peptides, molecular graphs, interaction networks)
• Familiarity with bioinformatics workflows, data preprocessing, and domain constraints
• Ability to bridge biological intuition and mathematical modeling

• Experience with equivariant or physics-informed models (e.g., geometric deep learning)
• Exposure to protein language models, molecular descriptors, or structural biology data
• Experience deploying or training models on cloud platforms (AWS, GCP, Azure)
• Master's or Ph.D. in Computer Science, Bioinformatics, Computational Biology, Applied Mathematics, Physics, or related fields

• Depth over buzzwords
• Mathematical clarity over black-box usage
• Curiosity, rigor, and the ability to question assumptions
• Prior real work on GNNs, not just coursework or tutorials
• If someone cannot explain their model mathematically, they are not a fit.

Moleculyst Ventures Private Limited is a deep-tech biotechnology startup operating at the intersection of computational biology, artificial intelligence, and genomics, with a mission to enable precision and personalized healthcare.

We focus on modeling complex biological systems by uncovering interactions between genetics, molecular structure, and environmental factors. Our work spans AI-driven diagnostics, therapeutic discovery, and preventive healthcare, leveraging graph-based representations, large-scale biological datasets, and first-principles reasoning. Moleculyst aims to empower clinicians and researchers with scientifically grounded, next-generation computational tools.

This is a remote contract role for a Deep Learning Specialist with prior hands-on experience in Graph Neural Networks (GNNs) and a strong mathematical foundation.

The role involves designing, implementing, and optimizing deep learning models for large-scale biological data, including molecular, genomic, and interaction networks. You will work closely with interdisciplinary teams spanning biology, chemistry, and data science to develop theoretically sound and practically effective models for real-world biomedical problems.

This is not a plug-and-play ML role - we are looking for individuals who understand why models work, not just how to run them.

• Design and implement deep learning architectures, with a strong emphasis on Graph Neural Networks (GNNs) for biological and molecular data
• Develop models for molecular graphs, protein/peptide structures, interaction networks, and omics data
• Research, evaluate, and implement state-of-the-art methods (message passing, equivariant models, attention mechanisms, representation learning)
• Perform rigorous model evaluation, ablation studies, and error analysis grounded in both math and domain context
• Automate training, evaluation, and experimentation pipelines for large datasets
• Collaborate with biologists and domain experts to translate biological hypotheses into computational models
• Analyze results to extract scientifically interpretable and actionable insights

• Strong understanding of deep learning fundamentals, including:
• Linear algebra, probability, optimization
• Backpropagation, loss functions, regularization
• Bias-variance tradeoffs and generalization
• Proven experience with Graph Neural Networks, such as:
• GCN, GAT, MPNN, Graph Transformers, or related architectures
• Practical understanding of message passing, aggregation, and graph representations
• Ability to derive, reason about, and modify model formulations, not just apply libraries

• Strong proficiency in Python
• Hands-on experience with PyTorch (preferred) and/or TensorFlow
• Familiarity with ML/DL tooling for experimentation, logging, and reproducibility

• Prior experience handling biological or molecular datasets (e.g., genomics, proteomics, peptides, molecular graphs, interaction networks)
• Familiarity with bioinformatics workflows, data preprocessing, and domain constraints
• Ability to bridge biological intuition and mathematical modeling

• Experience with equivariant or physics-informed models (e.g., geometric deep learning)
• Exposure to protein language models, molecular descriptors, or structural biology data
• Experience deploying or training models on cloud platforms (AWS, GCP, Azure)
• Master's or Ph.D. in Computer Science, Bioinformatics, Computational Biology, Applied Mathematics, Physics, or related fields

• Depth over buzzwords
• Mathematical clarity over black-box usage
• Curiosity, rigor, and the ability to question assumptions
• Prior real work on GNNs, not just coursework or tutorials
• If someone cannot explain their model mathematically, they are not a fit.

Moleculyst Ventures Private Limited is a deep-tech biotechnology startup operating at the intersection of computational biology, artificial intelligence, and genomics, with a mission to enable precision and personalized healthcare.

We focus on modeling complex biological systems by uncovering interactions between genetics, molecular structure, and environmental factors. Our work spans AI-driven diagnostics, therapeutic discovery, and preventive healthcare, leveraging graph-based representations, large-scale biological datasets, and first-principles reasoning. Moleculyst aims to empower clinicians and researchers with scientifically grounded, next-generation computational tools.

This is a remote contract role for a Deep Learning Specialist with prior hands-on experience in Graph Neural Networks (GNNs) and a strong mathematical foundation.

The role involves designing, implementing, and optimizing deep learning models for large-scale biological data, including molecular, genomic, and interaction networks. You will work closely with interdisciplinary teams spanning biology, chemistry, and data science to develop theoretically sound and practically effective models for real-world biomedical problems.

This is not a plug-and-play ML role - we are looking for individuals who understand why models work, not just how to run them.

• Design and implement deep learning architectures, with a strong emphasis on Graph Neural Networks (GNNs) for biological and molecular data
• Develop models for molecular graphs, protein/peptide structures, interaction networks, and omics data
• Research, evaluate, and implement state-of-the-art methods (message passing, equivariant models, attention mechanisms, representation learning)
• Perform rigorous model evaluation, ablation studies, and error analysis grounded in both math and domain context
• Automate training, evaluation, and experimentation pipelines for large datasets
• Collaborate with biologists and domain experts to translate biological hypotheses into computational models
• Analyze results to extract scientifically interpretable and actionable insights

• Strong understanding of deep learning fundamentals, including:
• Linear algebra, probability, optimization
• Backpropagation, loss functions, regularization
• Bias-variance tradeoffs and generalization
• Proven experience with Graph Neural Networks, such as:
• GCN, GAT, MPNN, Graph Transformers, or related architectures
• Practical understanding of message passing, aggregation, and graph representations
• Ability to derive, reason about, and modify model formulations, not just apply libraries

• Strong proficiency in Python
• Hands-on experience with PyTorch (preferred) and/or TensorFlow
• Familiarity with ML/DL tooling for experimentation, logging, and reproducibility

• Prior experience handling biological or molecular datasets (e.g., genomics, proteomics, peptides, molecular graphs, interaction networks)
• Familiarity with bioinformatics workflows, data preprocessing, and domain constraints
• Ability to bridge biological intuition and mathematical modeling

• Experience with equivariant or physics-informed models (e.g., geometric deep learning)
• Exposure to protein language models, molecular descriptors, or structural biology data
• Experience deploying or training models on cloud platforms (AWS, GCP, Azure)
• Master's or Ph.D. in Computer Science, Bioinformatics, Computational Biology, Applied Mathematics, Physics, or related fields

• Depth over buzzwords
• Mathematical clarity over black-box usage
• Curiosity, rigor, and the ability to question assumptions
• Prior real work on GNNs, not just coursework or tutorials
• If someone cannot explain their model mathematically, they are not a fit.

Moleculyst Ventures Private Limited is a deep-tech biotechnology startup operating at the intersection of computational biology, artificial intelligence, and genomics, with a mission to enable precision and personalized healthcare.

We focus on modeling complex biological systems by uncovering interactions between genetics, molecular structure, and environmental factors. Our work spans AI-driven diagnostics, therapeutic discovery, and preventive healthcare, leveraging graph-based representations, large-scale biological datasets, and first-principles reasoning. Moleculyst aims to empower clinicians and researchers with scientifically grounded, next-generation computational tools.

This is a remote contract role for a Deep Learning Specialist with prior hands-on experience in Graph Neural Networks (GNNs) and a strong mathematical foundation.

The role involves designing, implementing, and optimizing deep learning models for large-scale biological data, including molecular, genomic, and interaction networks. You will work closely with interdisciplinary teams spanning biology, chemistry, and data science to develop theoretically sound and practically effective models for real-world biomedical problems.

This is not a plug-and-play ML role - we are looking for individuals who understand why models work, not just how to run them.

• Design and implement deep learning architectures, with a strong emphasis on Graph Neural Networks (GNNs) for biological and molecular data
• Develop models for molecular graphs, protein/peptide structures, interaction networks, and omics data
• Research, evaluate, and implement state-of-the-art methods (message passing, equivariant models, attention mechanisms, representation learning)
• Perform rigorous model evaluation, ablation studies, and error analysis grounded in both math and domain context
• Automate training, evaluation, and experimentation pipelines for large datasets
• Collaborate with biologists and domain experts to translate biological hypotheses into computational models
• Analyze results to extract scientifically interpretable and actionable insights

• Strong understanding of deep learning fundamentals, including:
• Linear algebra, probability, optimization
• Backpropagation, loss functions, regularization
• Bias-variance tradeoffs and generalization
• Proven experience with Graph Neural Networks, such as:
• GCN, GAT, MPNN, Graph Transformers, or related architectures
• Practical understanding of message passing, aggregation, and graph representations
• Ability to derive, reason about, and modify model formulations, not just apply libraries

• Strong proficiency in Python
• Hands-on experience with PyTorch (preferred) and/or TensorFlow
• Familiarity with ML/DL tooling for experimentation, logging, and reproducibility

• Prior experience handling biological or molecular datasets (e.g., genomics, proteomics, peptides, molecular graphs, interaction networks)
• Familiarity with bioinformatics workflows, data preprocessing, and domain constraints
• Ability to bridge biological intuition and mathematical modeling

• Experience with equivariant or physics-informed models (e.g., geometric deep learning)
• Exposure to protein language models, molecular descriptors, or structural biology data
• Experience deploying or training models on cloud platforms (AWS, GCP, Azure)
• Master's or Ph.D. in Computer Science, Bioinformatics, Computational Biology, Applied Mathematics, Physics, or related fields

• Depth over buzzwords
• Mathematical clarity over black-box usage
• Curiosity, rigor, and the ability to question assumptions
• Prior real work on GNNs, not just coursework or tutorials
• If someone cannot explain their model mathematically, they are not a fit.

Moleculyst Ventures Private Limited is a deep-tech biotechnology startup operating at the intersection of computational biology, artificial intelligence, and genomics, with a mission to enable precision and personalized healthcare.

We focus on modeling complex biological systems by uncovering interactions between genetics, molecular structure, and environmental factors. Our work spans AI-driven diagnostics, therapeutic discovery, and preventive healthcare, leveraging graph-based representations, large-scale biological datasets, and first-principles reasoning. Moleculyst aims to empower clinicians and researchers with scientifically grounded, next-generation computational tools.

This is a remote contract role for a Deep Learning Specialist with prior hands-on experience in Graph Neural Networks (GNNs) and a strong mathematical foundation.

The role involves designing, implementing, and optimizing deep learning models for large-scale biological data, including molecular, genomic, and interaction networks. You will work closely with interdisciplinary teams spanning biology, chemistry, and data science to develop theoretically sound and practically effective models for real-world biomedical problems.

This is not a plug-and-play ML role - we are looking for individuals who understand why models work, not just how to run them.

• Design and implement deep learning architectures, with a strong emphasis on Graph Neural Networks (GNNs) for biological and molecular data
• Develop models for molecular graphs, protein/peptide structures, interaction networks, and omics data
• Research, evaluate, and implement state-of-the-art methods (message passing, equivariant models, attention mechanisms, representation learning)
• Perform rigorous model evaluation, ablation studies, and error analysis grounded in both math and domain context
• Automate training, evaluation, and experimentation pipelines for large datasets
• Collaborate with biologists and domain experts to translate biological hypotheses into computational models
• Analyze results to extract scientifically interpretable and actionable insights

• Strong understanding of deep learning fundamentals, including:
• Linear algebra, probability, optimization
• Backpropagation, loss functions, regularization
• Bias-variance tradeoffs and generalization
• Proven experience with Graph Neural Networks, such as:
• GCN, GAT, MPNN, Graph Transformers, or related architectures
• Practical understanding of message passing, aggregation, and graph representations
• Ability to derive, reason about, and modify model formulations, not just apply libraries

• Strong proficiency in Python
• Hands-on experience with PyTorch (preferred) and/or TensorFlow
• Familiarity with ML/DL tooling for experimentation, logging, and reproducibility

• Prior experience handling biological or molecular datasets (e.g., genomics, proteomics, peptides, molecular graphs, interaction networks)
• Familiarity with bioinformatics workflows, data preprocessing, and domain constraints
• Ability to bridge biological intuition and mathematical modeling

• Experience with equivariant or physics-informed models (e.g., geometric deep learning)
• Exposure to protein language models, molecular descriptors, or structural biology data
• Experience deploying or training models on cloud platforms (AWS, GCP, Azure)
• Master's or Ph.D. in Computer Science, Bioinformatics, Computational Biology, Applied Mathematics, Physics, or related fields

• Depth over buzzwords
• Mathematical clarity over black-box usage
• Curiosity, rigor, and the ability to question assumptions
• Prior real work on GNNs, not just coursework or tutorials
• If someone cannot explain their model mathematically, they are not a fit.

Yotta Data Services is powering Digital Transformation with Scalable Cloud, Colocation, and Managed Services.

Yotta Data Services offers a comprehensive suite of cloud, data center, and managed services designed to accelerate digital transformation for businesses of all sizes. With state-of-the-art infrastructure, cutting-edge AI capabilities, and a commitment to data sovereignty, we empower organizations to innovate securely and efficiently.

Total /Relevant Experience

1 to 3 Years

Key Responsibilities

The key role of this profile is to work with customers directly and make them aware of technics available in DL and make best use of NVidia h/w & s/w. This includes installation and configuration of Nvidia DGX-family and different software stack (i.e. TRT/DS/usage of Tensorcore/AMP/NGC/Monitoring tools/Debugging tools/CUDA/other library & SDK/SLURM/Kubernetes). The role also includes few levels of project guidance and working jointly with customer on their journey of adopting right deep learning technics (both Training and Edge/Inferencing stack).

Must-have skill

• Well experienced with one of the Deep learning framework: TensorFlow/PyTorch/MXNet/Caffe
• Should have done one project using Nvidia h/w and DL framework
• Good knowledge on H/w, OS troubleshooting & Shell scripting
• Well understanding on Hardware and willing to learn Dat

Exciting opportunity to join our AI Teams in Chennai.

Position Available:

- Deep Learning Engineer (2+ years, Immediate joiners)

We are looking for professionals with the following skills:

- Proficiency in AI/ML (DL) and Image Processing fundamentals

- Strong understanding of Data structures and Algorithms

- Hands-on expertise with OpenCV, Numpy, PIL libraries

- Ability to develop Deep Learning models for computer vision challenges on resource-constrained devices

- Familiarity with Pytorch or Tensorflow frameworks

- Bonus: Experience in ADAS domain

- Knowledge of Camera models and birds-eye view projection is a plus.

Interested candidates can submit their resumes to arathy.rajkumar@ignitarium.com.

Moleculyst Ventures Private Limited is a deep-tech biotechnology startup operating at the intersection of computational biology, artificial intelligence, and genomics , with a mission to enable precision and personalized healthcare .

We focus on modeling complex biological systems by uncovering interactions between genetics, molecular structure, and environmental factors . Our work spans AI-driven diagnostics, therapeutic discovery, and preventive healthcare, leveraging graph-based representations, large-scale biological datasets, and first-principles reasoning . Moleculyst aims to empower clinicians and researchers with scientifically grounded, next-generation computational tools.

This is a remote contract role for a Deep Learning Specialist with prior hands-on experience in Graph Neural Networks (GNNs) and a strong mathematical foundation .

The role involves designing, implementing, and optimizing deep learning models for large-scale biological data , including molecular, genomic, and interaction networks. You will work closely with interdisciplinary teams spanning biology, chemistry, and data science to develop theoretically sound and practically effective models for real-world biomedical problems.

This is not a plug-and-play ML role — we are looking for indivi

We are hiring a Sr. Deep Learning Engineer for a technology-driven organization specializing in Industrial Automation, Robotics Intelligence, and AI-driven engineering solutions for global manufacturing environments. The role focuses on developing advanced AI models to enhance perception, autonomy, and robotics precision.

Experience: 3–8 Years

Location: Pune (5 days/Week)

Education: B.E or B.Tech

Role Brief:

This role involves building and deploying deep learning solutions for industrial robotic systems, ensuring accuracy, speed, and real-time intelligent performance across robotic arms and automated systems.

Mandatory Requirements:

• Industrial Robotic Arms experience

• Strong expertise in Kinematics and Motion Dynamics

• Deep Learning & Computer Vision expertise

• Experience in Industrial Robotics environments

Key Responsibilities:

• Develop and deploy DL models for robotics perception and automation

• Work on kinematics-based motion planning and robotic behavior optimization

• Implement Edge AI on embedded platforms such as NVIDIA Jetson

• Integrate AI systems with sensors, cameras, and IoT devices

• Optimize performance, latency, and reliability

• Work collaboratively with Robotics and AI engineering teams

Technical Skill

Join Nanonets to push the boundaries of what's possible with deep learning. We're not just implementing models – we're setting new benchmarks in document AI, with our open-source models achieving nearly 1 million downloads on Hugging Face and recognition from global AI leaders.

Backed by $40M+ in total funding including our recent $29M Series B from Accel, alongside Elevation Capital and Y Combinator, we're scaling our deep learning capabilities to serve enterprise clients including Toyota, Boston Scientific, and Bill.com. You'll work on genuinely challenging problems at the intersection of computer vision, NLP, and generative AI.

Here's a quick 1-minute intro video .

About the role

The role can be summed up as building and deploying cutting edge generalised deep learning architectures that can solve complex business problems like converting unstructured data into structured format without hand-tuning features/models. You are expected to build state of the art models that are best in the world for solving these problems, continuously experimenting and incorporating new advancements in the field into these architectures.

What we’re looking for

• 5-8 years of experience in Deep Learning.
• Strong foundational knowledge in deep learning concepts and architectures (LLMs and VLMs)
• Demonstrated expertise in at least one specialised area of deep l