1 day ago
Munich, GermanyStaff+
Base Salary
$100k - $250k/yr
Responsibilities
- Build the ML function from the ground up across post-training, agent systems, and production infrastructure.
- Structure, filter, and score experimental trajectories for training-data pipelines.
- Design and implement evaluations and benchmarks for model reasoning, planning, and experimental progress.
- Build agent environments, tool interfaces, observability systems, and replay infrastructure.
- Establish validation and provenance tracking for trajectory and data quality.
- Set priorities for the ML roadmap, experiments, systems, and hiring.
- Guide the technical direction of the growing ML team.
Requirements
- At least 3 years of professional machine learning engineering experience building or delivering production ML systems.
- Professional experience in post-training or model evaluations, including hands-on training-data structuring, filtering, and scoring.
- Production software engineering experience with Python and systems-level programming for ML infrastructure.
- Experience designing and implementing ML evaluation frameworks and benchmarks.
- Strong knowledge of trajectory data, reward structures, tool use, and agent decision-making.
- Experience with agent environments, replay and debugging tools, reinforcement-learning training systems, or data-validation and observability infrastructure.
- Reinforcement-learning implementation and tuning experience, frontier AI research, or experience with scaled post-training and evaluation teams is valuable.
- Ability to own priorities and work across research and production in an ambiguous environment.
Benefits
- Base salary of $100,000 to $250,000 USD annually.
- Visa sponsorship is not available.
- On-site role in Munich, Germany; Zurich, Switzerland, and San Francisco, California are also listed, and remote arrangements may be discussed.