ML инженер

Clera is a seed-stage deeptech startup operating in AI-driven materials acceleration and cleantech. It develops agentic AI systems for scientific discovery, connecting predictive models with physical experiments and laboratory automation.

• Design and implement agentic systems for materials discovery workflows involving experiments, simulations, and scientific datasets;
• Build decision-making systems that select next actions under uncertainty and determine when autonomy should act versus when humans should stay in the loop;
• Implement planning, control logic, and uncertainty-aware decision-making for physical systems and experimental constraints;
• Encode operational, experimental, and safety constraints into agent behavior; define stopping criteria, fallback strategies, and recovery mechanisms;
• 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;
• Instrument agents with logging, monitoring, and diagnostics;
• Build evaluation frameworks for decision quality, learning efficiency, and overall system behavior;
• 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.

• 4–8 Years of hands-on ML engineering experience, preferably with autonomous agents or decision-making systems in production or applied research;
• Experience designing and implementing agent-based systems for real-world workflows, including planning, action selection under uncertainty, and stopping, fallback, and recovery logic;
• Strong track record delivering production-grade ML systems with observability, logging, monitoring, and diagnostics;
• Experience integrating ML/AI models with lab automation, scientific instrumentation, or hardware/software systems;
• Proficiency in Python and at least one major ML framework such as PyTorch, TensorFlow, or JAX;
• Strong data tooling skills, including NumPy and SciPy;
• Background in scientific or structured data modeling rather than language-model-first systems;
• Knowledge of safety constraints and safety-aware validation practices for autonomous decision-making in physical environments;
• Strong cross-functional communication skills across AI research, engineering, and laboratory teams;
• English fluency;
• Right to work in Germany without employer sponsorship; visa sponsorship is not available;
• Nice to have: Experience in materials science, chemistry, or adjacent physical sciences, probabilistic reasoning, Bayesian optimization, active learning, reinforcement learning, model-based planning, control theory, and additional European language skills.

• On-site position in Berlin, Germany;
• Right to work in Germany without employer visa sponsorship is required.