About this role
About the Role
Own the machine learning function at an early-stage AI company, working with founders and researchers to shape post-training and agent systems. You will set technical priorities and help guide the growth of the ML team.
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 ML roadmap priorities across systems, experiments, and hiring.
- Lead the technical direction of the ML team as it grows.
What We're Looking For
- At least 3 years of professional machine learning engineering experience building or delivering production ML systems.
- Strong machine learning and software engineering skills, including production experience with Python and systems-level programming for ML infrastructure.
- Hands-on experience with post-training ML, including data pipelines for structuring, filtering, and scoring training data.
- Experience designing and implementing evaluation frameworks and benchmarks for ML models.
- Knowledge of trajectories, reward structures, tool use, and agent environment design.
- Experience with reinforcement learning, agent environments, replay systems, debugging tools, data validation, provenance, or ML pipeline observability.
- Background in frontier AI research or scaled post-training and evaluation teams is valuable.
- Comfort bridging research and production, taking ownership, and working in an ambiguous environment.
Compensation & Benefits
Salary range: $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.