About this role
About the Role
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.
What You'll Do
- 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.
What We're Looking For
- 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.
Compensation & Benefits
Salary range: $100,000 to $200,000 USD annually. Visa sponsorship is not available.
Location
On-site role based primarily in Munich, Germany, with additional offices in Zurich and San Francisco. Remote arrangements may be discussed on a case-by-case basis.