ML Engineer, Agents & Reasoning
CleraIndustrial Electrification company
Berlin, GermanySenior
Data & AI
About the role
TL;DR
Build agentic ML systems for materials discovery workflows.
- •Join a cross-functional team at the intersection of AI, engineering, and laboratory automation to build agentic ML systems that reason, plan, and act inside real materials discovery workflows.
- •You'll turn predictive models into reliable, operational decision-making agents capable of operating on messy physical experiments
- •embedding autonomy, safety, and observability directly into scientific discovery pipelines.
- •Key Responsibilities Design and implement agentic systems that plan, reason, and act across 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 humans should stay in the loop.
- •Implement planning, control logic, and uncertainty-aware decision-making tailored to physical systems.
- •Encode operational, experimental, and safety constraints directly into agent behavior.
- •Requirements 4–8 years of experience building ML-driven or algorithmic decision-making systems.
- •Strong background in scientific or structured data modeling.
- •Experience with planning, control, optimization, probabilistic reasoning, or decision-making under uncertainty.
- •Proficiency in modern ML frameworks (e.g., PyTorch, JAX) and strong general software engineering skills.
- •Comfortable owning systems end-to-end
- •from prototype to reliable, production-grade operation.
Required skills
PythonPyTorchJAX
Domain expertise
deeptechcleantechai
Tech stack
PythonPyTorchJAX