About the Role

As the founding machine learning engineer at an early-stage AI company, you will build the ML function from the ground up. You will shape post-training and agent systems, connecting research with reliable production infrastructure.

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 to support trajectory and data quality.

Set priorities for the ML roadmap, experiments, systems, and hiring.

Guide 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.

Professional experience in post-training or model evaluations, including hands-on work structuring, filtering, and scoring training data.

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 building agent environments, replay and debugging tools, RL training systems, or data validation and observability infrastructure.

Reinforcement learning implementation and tuning experience is valuable, as is experience in frontier AI research or scaled post-training and evaluation teams.

Comfort owning priorities and working across research and production in an ambiguous environment.

Compensation & Benefits

Base salary: $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; remote arrangements may be discussed.