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.
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.
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.
Salary range: $100,000 to $250,000 USD annually. Visa sponsorship is not available.
On-site in Munich, Germany. Other listed locations include Zurich, Switzerland, and San Francisco, California.