SLAM / Spatial Perception | AI / ML Engineer / Munich in München

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