About the Role

We are a seed-stage deeptech startup at the intersection of AI, robotics, and materials science, building an advanced platform that dramatically accelerates the discovery of new materials — particularly for the energy sector. 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 Do

Design 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.
Collaborate with AI researchers to embed predictive models into agent workflows and translate model outputs into executable actions.
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 model accuracy.
Analyze failure cases and iterate on system design based on real-world experimental outcomes.
Own systems end-to-end: from prototype through deployment and ongoing operation.

What We're Looking For

Required

4–8 years of experience building ML-driven or algorithmic decision-making systems in production or applied research settings.
Strong background in scientific or structured data modeling (rather than language-first or NLP-heavy systems).
Experience with planning, control, optimization, probabilistic reasoning, or decision-making under uncertainty.
Proficiency in modern ML frameworks such as PyTorch or JAX, paired with strong general software engineering skills.
Comfortable owning systems end-to-end, from early prototype through to reliable production operation.
Ability to reason clearly about system behavior in complex, partially observable environments.
Clear communicator who can collaborate effectively across AI, engineering, and scientific teams.
English fluency (additional language skills a plus).

Nice to Have

Technical curiosity about physical systems, laboratory experiments, and real-world constraints.
Experience in materials science, chemistry, cleantech, or adjacent scientific domains.
Familiarity with laboratory automation or robotics integration.
Additional European language skills (German in particular).

Location & Work Arrangement

This role is on-site in Berlin, Germany. We work closely as a team in person, and we expect this role to be based full-time at our Berlin office. Visa sponsorship is not available.

Why This Role

Work on genuinely hard AI problems at the frontier of scientific discovery and physical-world autonomy.
Join an early-stage, mission-driven team where your work directly shapes both the product and the culture.
Collaborate across AI research, engineering, and laboratory science in ways that are rare in a single role.
Contribute to technology with meaningful real-world impact in the energy transition and advanced manufacturing.

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