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About the Role

This is a hands-on ML engineering role focused on building agentic AI systems for scientific discovery. You will create systems that reason, plan, and act within materials discovery workflows, transforming predictive models into operational decision-making agents that interact with physical experiments and laboratory automation. The role is at the intersection of AI research, software engineering, and lab science, emphasizing autonomy, safety, and observability in discovery pipelines.

Responsibilities

Design and implement agentic systems for materials discovery workflows.
Build decision-making systems for action selection under uncertainty.
Implement planning, control logic, and uncertainty-aware decision-making for physical systems.
Encode operational, experimental, and safety constraints into agent behavior.
Collaborate with AI researchers to embed predictive models into agent workflows.
Integrate agents with laboratory automation and software systems.
Instrument agents with logging, monitoring, and diagnostics.
Build evaluation frameworks for decision quality and system behavior.
Analyze failure cases and iterate on system design.
Own systems end-to-end, from prototype to production deployment and operation.

Requirements

4–8 years of hands-on ML engineering experience, preferably with autonomous agents or decision-making systems.
Experience designing and implementing agent-based systems for real-world workflows.
Strong track record delivering production-grade ML systems with an emphasis on 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 (e.g., PyTorch, TensorFlow, or JAX).
Strong data tooling skills (NumPy, SciPy, etc.).
Background in scientific or structured data modeling.
Knowledge of safety constraints and safety-aware validation practices for autonomous decision-making in physical environments.
Strong cross-functional communication skills.
English fluency.
Right to work in Germany without employer sponsorship.

Skills

Python
PyTorch
TensorFlow
JAX
NumPy
SciPy
ML engineering
Autonomous agents
Decision-making systems
Agent-based systems
Observability
Logging
Monitoring
Diagnostics
Lab automation integration
Scientific instrumentation integration
Hardware/software system integration
Scientific data modeling
Structured data modeling
Safety constraints
Safety-aware validation
Cross-functional communication

Location

Berlin, Germany

Work Type

On-site
Full-time

Experience Level

4-8 years

About the Company

Seed-stage deeptech startup operating in the AI-driven materials acceleration and cleantech space.
Small but highly experienced team with institutional backing.