Our work combines physics-informed AI, autonomous laboratory systems, and rich multi-modal experimental data to compress decades-long R&D timelines into years.
As an ML Engineer, Agents & Reasoning, you will design and build the agentic AI systems that sit at the heart of our materials discovery workflows.
You'll turn predictive models into reliable, operational decision-making agents that work alongside physical experiments, robotic systems, and scientific datasets.
This is a high-ownership, end-to-end role on a small, cross-functional team of ~12–60 people based in Berlin, Germany (on-site).
Please note: visa sponsorship is not available for this role.
What You'll DoDesign and implement agentic systems that plan, reason, and act across real materials discovery workflows.
Build decision-making systems that operate over experiments, simulations, and scientific datasets.
Select next actions under uncertainty and encode when autonomy should act versus when a human should stay in the loop.
Implement planning, control logic, and uncertainty-aware decision-making tailored to physical systems and lab environments.
Encode operational, experimental, and safety constraints directly into agent behavior.
Define stopping criteria, fallback strategies, and recovery mechanisms to prevent brittle behavior.
Integrate agents with laboratory automation and software systems so agent outputs drive real-world actions.
Instrument agents with logging, monitoring, and diagnostics to support observability and debugging.
Build evaluation frameworks that assess decision quality, learning efficiency, and system behavior — beyond simple.