11 hours ago
Munich, GermanyStaff+
Base Salary
$100k - $200k/yr
Responsibilities
- Structure, filter, and score experimental trajectories to create high-quality training-data pipelines.
- Design and implement evaluation frameworks and benchmarks for model reasoning, planning, and improvement.
- Build agent environments, tool interfaces, observability systems, and replay infrastructure.
- Establish validation and provenance tracking for trajectory and data quality.
- Set ML roadmap priorities across systems, experiments, and hiring decisions.
- Lead and grow the ML team as the company scales.
Requirements
- At least 3 years of machine learning engineering experience delivering production ML systems.
- Hands-on experience with post-training data pipelines, including structuring, filtering, and scoring training data.
- Direct experience designing and implementing ML model evaluations or benchmarks.
- Strong Python skills and comfort with systems-level programming for ML infrastructure.
- Deep understanding of trajectory data, reward modeling, and agent decision-making systems.
- Experience implementing and tuning reinforcement learning algorithms.
- Experience building replay systems, debugging tools, and observability systems for agent trajectories.
- Ability to bridge research and production in an ambiguous, high-agency environment.
- Experience at a frontier AI lab or on a post-training or evaluations team is preferred.
Benefits
- Salary range of $100,000 to $200,000 USD annually.
- Primary location is Munich, Germany, with additional offices in Zurich and San Francisco.
- On-site role; remote arrangements may be discussed case by case.