About the Role
As the founding machine learning engineer at an early-stage AI company, you will build the ML function from the ground up. You will shape post-training and agent systems, connecting research with reliable production infrastructure.
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 evaluations and benchmarks for model reasoning, planning, and experimental progress.
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Build reliable agent environments, tool interfaces, observability systems, and replay infrastructure.
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Establish validation and provenance tracking to support trajectory and data quality.
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Set priorities for the ML roadmap, experiments, systems, and hiring.
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Guide the technical direction of the ML team as it grows.
What We're Looking For
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At least 3 years of professional machine learning engineering experience building or delivering production ML systems.
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Professional experience in post-training or model evaluations, including hands-on work structuring, filtering, and scoring training data.
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Production software engineering experience with Python and systems-level programming for ML infrastructure.
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Experience designing and implementing ML evaluation frameworks and benchmarks.
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Strong knowledge of trajectory data, reward structures, tool use, and agent decision-making.
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Experience building agent environments, replay and debugging tools, RL training systems, or data validation and observability infrastructure.
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Reinforcement learning implementation and tuning experience is valuable, as is experience in frontier AI research or scaled post-training and evaluation teams.
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Comfort owning priorities and working across research and production in an ambiguous environment.
Compensation & Benefits
Base salary: $100,000 to $250,000 USD annually. Visa sponsorship is not available.
Location
On-site in Munich, Germany. Other listed locations include Zurich, Switzerland, and San Francisco, California; remote arrangements may be discussed.
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