About the Role
Own the machine learning function at an early-stage AI company, shaping post-training and agent systems from research through production. You will help set technical priorities and build the foundations for the ML team as it grows.
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 priorities for ML systems, experiments, and hiring, and lead technical direction as the team scales.

What We’re Looking For

• At least 3 years of professional experience building or delivering production machine learning systems.

• Hands-on experience with post-training ML, evaluation frameworks, and post-training data pipelines, including structuring, filtering, and scoring training data.

• Strong Python and systems-level software engineering skills for ML infrastructure.

• Experience with agents, reinforcement learning, trajectory data, reward structures, and agent environment design.

• Experience building evaluation benchmarks, replay and debugging tools, data validation, provenance tracking, or observability systems.

• Ability to bridge research and production, take ownership, and work effectively in an ambiguous, fast-moving environment.

Compensation & Benefits
Salary range: $100,000 to $250,000 USD annually. Visa sponsorship is not available.
Location
On-site in Munich, Germany.

About the Role
Own the machine learning function at an early-stage AI company, shaping post-training and agent systems from research through production. You will help set technical priorities and build the foundations for the ML team as it grows.
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 priorities for ML systems, experiments, and hiring, and lead technical direction as the team scales.

What We’re Looking For

• At least 3 years of professional experience building or delivering production machine learning systems.

• Hands-on experience with post-training ML, evaluation frameworks, and post-training data pipelines, including structuring, filtering, and scoring training data.

• Strong Python and systems-level software engineering skills for ML infrastructure.

• Experience with agents, reinforcement learning, trajectory data, reward structures, and agent environment design.

• Experience building evaluation benchmarks, replay and debugging tools, data validation, provenance tracking, or observability systems.

• Ability to bridge research and production, take ownership, and work effectively in an ambiguous, fast-moving environment.

Compensation & Benefits
Salary range: $100,000 to $250,000 USD annually. Visa sponsorship is not available.
Location
On-site in Munich, Germany.