Senior Research Scientist, Geometric Deep Learning and Scientific ML
Location: Berlin, Germany
Working model: Full time, hybrid
About the company
We are supporting an early stage deep learning company developing foundation models for engineering and physical systems.
The company works with industrial partners across mechanical engineering, electrical engineering, manufacturing, and engineering design. Its goal is to create models that can generalise across different physical problems, geometries, and industrial constraints.
This is a small research led team where new ideas can be implemented, tested, and improved quickly.
The opportunity
We are looking for a Senior Research Scientist who can develop original deep learning methods for complex engineering problems.
This is not a role focused on applying existing models without questioning them. You will be expected to understand why an architecture works, identify where current methods fail, and develop new approaches from first principles.
You should have deep research expertise in at least one relevant area, while being willing to work across adjacent fields as projects develop.
What you could work on
Designing and validating new geometric deep learning architectures
Developing models for graphs, meshes, point clouds, particles, surfaces, and other structured data
Building generative models for engineering design and physical systems
Developing surrogate models for computationally expensive simulations
Training models using synthetic and simulated data
Conditioning generative models on multimodal inputs and physical constraints
Exploring neural operators, neural differential equations, diffusion models, and function space modelling
Studying network architecture, optimiser behaviour, regularisation, loss geometry, and generalisation
Translating research papers into reliable experimental systems
Working directly with industrial simulation environments and proprietary engineering datasets
What we are looking for
A PhD in computer science, mathematics, applied mathematics, physics, engineering, or a related subject
Research depth in geometric deep learning, scientific machine learning, generative modelling, synthetic data, surrogate modelling, neural operators, or a closely related area
Strong mathematical understanding of neural networks, optimisation, and generalisation
Evidence that you can develop original methods rather than only reproduce existing research
Experience designing, training, and evaluating deep learning architectures
Ability to read papers critically and explain the reasoning behind technical decisions
Strong Python experience with PyTorch, JAX, TensorFlow, or similar frameworks
Interest in applying research to difficult industrial problems
Candidates completing a strong PhD, experienced postdoctoral researchers, and researchers with several years of industry experience are all encouraged to apply.
Useful additional experience
Three dimensional geometry, meshes, point clouds, or particle systems
Physics informed learning and differentiable simulation
Reinforcement learning for optimisation or trajectory generation
Procedural geometry generation
Simulated data transfer into real applications
Experience moving research models into production environments
Previous CAD, CAM, CNC, or manufacturing experience is not required.
What is offered
Direct influence over the company’s research direction
Close collaboration with the founders
Freedom to explore and test original ideas
Access to industrial datasets and simulation environments
Short research and development cyclesConference attendance and continued learning support
Potential equity participation
Possible relocation support
English speaking working environment, with no German requirement
The team works from Berlin, with approximately two to three days per week available to work from home.
If you enjoy developing new deep learning methods, reasoning from mathematical foundations, and applying research to physical engineering problems, we would be interested in speaking with you.
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