I'm exclusively representing an early-stage, deep-tech Physical AI company based in Munich that is building technology at the intersection of AI, robotics and industrial automation .
The company is developing a new approach to how intelligent machines learn from the physical world - using real-world multi modal data captured from industrial environments to improve how AI systems understand, assist and ultimately automate complex manufacturing processes.
Backed by a leading Munich-based deep-tech VC and having recently secured multi-million-dollar seed funding , the company is now moving from successful industrial pilots into the next phase of product development, industrialisation and commercialisation.
They are looking to add a Senior AI/ML Engineerto their small, highly technical team.
The role
Our clients accept our system through formal tests with hard recall and false-positive gates - so our models must provably work: which weights, trained on which data, with which config, always answerable. You will own that machinery end-to-end, and with it two of our hardest ML problems: synthetic data generation for rare anomalies, and spatial reasoning - knowing which vehicle a worker is acting on as they move between cars, using SLAM fused with vehicle identity signals.
You'll work as a peer of our Lead ML Engineer - they own what the system should do, you own how models get built, trained, and reproduced - designing together, in the open, with a direct line to the CTO.
What you'll do
Build the machinery that makes models provable - training pipelines, experiment tracking, model registry: full lineage from dataset to deployed weights, plus the evaluation harnesses we stake client acceptance on
Solve data scarcity - simulation-based synthetic data pipelines for anomaly classes real factories are too good to produce often
Take the system beyond vision - productionize our audio modality; develop worker-vehicle association with SLAM and vehicle identity signals
Ship at the edge - own the anonymization models our privacy guarantees depend on; optimize everything for constrained GPU/CPU budgets on factory hardware
What you get
The ML production culture of a company, shaped by you from the start - registry, tracking, evals, your way
Multimodal problems (vision, audio, spatial) most teams only get one of, on data nobody else has
Your models on real assembly lines at major OEMs within weeks, with measurable stakes
Where you'll be in 12 months
Every model that passes client acceptance is reproducible from the registry. A rare-anomaly class hit its recall gate on synthetic data. Audio is live in a deployment, and SLAM-based worker-vehicle association is validated on a real line. We'll get there together - the architecture with our Lead ML Engineer, the machinery yours.
Who you are
You report the real number, especially when it's bad - our clients' acceptance tests leave no room for flattering evals
You build machines that build models: reproducibility over heroics
You prefer solving a problem once, generally, over solving it five times quickly
You explore broadly, then converge and commit
You're creative about data scarcity - synthesis, augmentation, simulation
Your experience
Must have:
Strong PyTorch and production ML experience (detection / classification / tracking)
SLAM / spatial perception used in production, not just coursework
Model optimization for edge hardware (e.g. ONNX, TensorRT)
Ways to stand out:
Manufacturing, robotics, or other physical-world domains
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