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
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Structure, filter, and score experimental trajectories to build high-quality training data pipelines.
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Design and implement evals and benchmarks that measure model reasoning, planning, and experimental improvement.
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Build reliable agent environments, tool interfaces, observability systems, and replay infrastructure.
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Establish robust validation and provenance tracking for trajectory and data quality.
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Set ML roadmap priorities across systems, experiments, and hiring decisions.
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Lead and grow the ML team's technical direction as the company scales.
What We're Looking For
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3+ years of machine learning engineering experience delivering production ML systems.
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Hands-on experience with post-training data pipelines, including structuring, filtering, and scoring training data.
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Demonstrated experience building agent environments, tool interfaces, and RL training systems.
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Strong Python and systems-level programming skills for ML infrastructure.
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Experience designing and implementing evaluation frameworks and benchmarks for ML models.
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Deep understanding of trajectory data, reward modeling, and agent decision-making systems.
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Experience building data validation, provenance tracking, and observability systems for ML pipelines.
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High agency, comfort with ambiguity, and the ability to bridge research and production seamlessly.
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Background at a frontier AI lab or on a post-training or evals team is a strong plus.
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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.
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