Founding Machine Learning Engineer

This is a founding ML engineering role at an early-stage AI startup, giving you full ownership of the ML function from day one. Working directly with founders and researchers, you will shape the technical direction of post-training pipelines and agent systems while building the team around you.

• Structure, filter, and score experimental trajectories to build high-quality training data pipelines.
• Design and implement evals and benchmarks that measure model reasoning, planning, and experimental improvement.
• Build reliable agent environments, tool interfaces, observability systems, and replay infrastructure.
• Establish robust validation and provenance tracking for trajectory and data quality.
• Set ML roadmap priorities across systems, experiments, and hiring decisions.
• Lead and grow the ML team's technical direction as the company scales.

• 3+ years of machine learning engineering experience delivering production ML systems.
• Hands-on experience with post-training data pipelines, including structuring, filtering, and scoring training data.
• Demonstrated experience building agent environments, tool interfaces, and RL training systems.
• Strong Python and systems-level programming skills for ML infrastructure.
• Experience designing and implementing evaluation frameworks and benchmarks for ML models.
• Deep understanding of trajectory data, reward modeling, and agent decision-making systems.
• Experience building data validation, provenance tracking, and observability systems for ML pipelines.
• High agency, comfort with ambiguity, and the ability to bridge research and production seamlessly.
• Background at a frontier AI lab or on a post-training or evals team is a strong plus.
• Experience with reinforcement learning algorithm implementation, replay systems, or agent debugging tools is a plus.

Salary range: $100,000 to $200,000 USD annually. Visa sponsorship is not available.

On-site role based primarily in Munich, Germany, with additional offices in Zurich and San Francisco. Remote arrangements may be discussed on a case-based basis.