About the Role
This is a founding ML engineering role at an early-stage AI startup, where you will own the machine learning function from day one. Working closely with founders and researchers, you will shape the technical direction of post-training pipelines, agent systems, and the broader ML roadmap as the team scales.
What You'll Do
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Structure, filter, and score experimental trajectories for 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 the technical direction of the ML team as it grows.
What We're Looking For
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3 or more years in machine learning engineering roles with a track record of 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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Production software engineering proficiency in Python and systems-level programming 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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Background at a frontier AI lab or on a post-training or evals team at scale is a strong plus.
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High agency, comfort with ambiguity, and the ability to bridge research and production seamlessly.
Compensation & Benefits
Salary range: $100,000 to $170,000 USD annually. Visa sponsorship is not available.
Location
On-site in Munich, Germany (primary location), with additional offices in Zurich, Switzerland and San Francisco, California. Remote arrangements may be discussed on a case-by-case basis.
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