About the Role
Own the machine learning function at an early-stage AI company, shaping post-training and agent systems from research through production. You will help set technical priorities and build the foundations for the ML team as it grows.
What You'll Do
Structure, filter, and score experimental trajectories for training data pipelines.
Design and implement evaluations and benchmarks for model reasoning, planning, and experimental progress.
Build reliable agent environments, tool interfaces, observability systems, and replay infrastructure.
Establish validation and provenance tracking for trajectory and data quality.
Set priorities for ML systems, experiments, and hiring, and lead technical direction as the team scales.
What We're Looking For
At least 3 years of professional experience building or delivering production machine learning systems.
Hands-on experience with post-training ML, evaluation frameworks, and post-training data pipelines, including structuring, filtering, and scoring training data.
Strong Python and systems-level software engineering skills for ML infrastructure.
Experience with agents, reinforcement learning, trajectory data, reward structures, and agent environment design.
Experience building evaluation benchmarks, replay and debugging tools, data validation, provenance tracking, or observability systems.
Ability to bridge research and production, take ownership, and work effectively in an ambiguous, fast-moving environment.
Compensation & Benefits
Salary range: $100,000 to $250,000 USD annually. Visa sponsorship is not available.
Location
On-site in Munich, Germany.