ML Engineer, Agents & Reasoning

Job description

About the Role This is a hands-on ML engineering role at the frontier of agentic AI for scientific discovery . You'll build systems that reason, plan, and act inside real materials discovery workflows — turning predictive models into reliable, operational decision-making agents that work directly with physical experiments and laboratory automation. You'll sit at the intersection of AI research, software engineering, and lab science, embedding autonomy, safety, and observability into end-to-end discovery pipelines. The company is a seed-stage deeptech startup operating in the AI-driven materials acceleration and cleantech space, with a small but highly experienced team and institutional backing. This is an on-site role based in Berlin, Germany . Right to work in Germany without employer visa sponsorship is required. What You'll Do Design and implement agentic systems that plan, reason, and act across materials discovery workflows involving experiments, simulations, and scientific datasets. Build decision-making systems that select next actions under uncertainty and encode when autonomy should act versus when humans should stay in the loop. Implement planning, control logic, and uncertainty-aware decision-making tailored to physical systems and experimental constraints. 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 real-world actions. Integrate agents with laboratory automation and software systems so decisions translate into physical outcomes. Instrument agents with logging, monitoring, and diagnostics to ensure observability and support debugging. Build evaluation frameworks that assess decision quality, learning efficiency, and overall system behavior — going beyond model accuracy alone. Analyze failure cases and iterate on system design based on real-world operational outcomes. Own systems end-to-end: from prototype through production deployment and ongoing operation. What We're Looking For Required: 4–8 years of hands-on ML engineering experience, preferably with autonomous agents or decision-making systems in production or applied research settings. Demonstrated experience designing and implem…