Founding Machine Learning Engineer

Clera; München; Vor Ort; EUR 88.000 - 176.000; Vollzeit

Zusammenfassung

Clera, an early-stage AI startup, is seeking a founding ML engineering leader to own the ML function from day one. You will collaborate directly with the founders and researchers to shape post-training pipelines and agent systems, building the team around you.

You will set the ML roadmap, design evaluation frameworks, and develop RL tooling, while bridging research and production at scale in Munich and via offices in Zurich and San Francisco.

Qualifikationen

• 3+ years of production ML engineering experience.
• Hands-on in post-training data pipelines.
• Experience building agent environments and RL training systems.
• Strong Python and ML infra skills.
• Design/evaluate ML benchmarks for models.
• Understanding trajectory data, reward modeling, and agent decisions.
• Data validation, provenance tracking, and observability for ML pipelines.
• High agency, comfortable with ambiguity, bridge research and production.
• Background at frontier AI lab or evals team is a plus.
• Experience with RL replay tools or agent debugging tools is a plus.

Aufgaben

• Structure, filter, and score experimental trajectories to build high-quality training data pipelines.
• Design evals and benchmarks measuring model reasoning, planning, and improvements.
• Build reliable agent environments, tool interfaces, observability systems, and replay infrastructure.
• Establish robust 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's technical direction as the company scales.

Kenntnisse

ML engineering

Post-training data pipelines

Agent environments

Python programming

Evaluation frameworks

Trajectory data

Data validation & provenance

Research-to-production bridging

RL algorithms & replay tools

Jobbeschreibung

About The Role

This is a founding ML engineering role at an early-stage AI startup, giving you full ownership of the ML function from day one. Working directly with founders and researchers, you will shape the technical direction of post-training pipelines and agent systems while building the team around you.

What You\'ll Do

• Structure, filter, and score experimental trajectories to build high-quality training data pipelines.
• Design and implement evals and benchmarks that measure model reasoning, planning, and experimental improvement.
• Build reliable agent environments, tool interfaces, observability systems, and replay infrastructure.
• Establish robust 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\'s technical direction as the company scales.

What We\'re Looking For

• 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.
• Demonstrated experience building agent environments, tool interfaces, and RL training systems.
• Strong Python and systems-level programming skills for ML infrastructure.
• Experience designing and implementing evaluation frameworks and benchmarks for ML models.
• Deep understanding of trajectory data, reward modeling, and agent decision-making systems.
• Experience building data validation, provenance tracking, and observability systems for ML pipelines.
• High agency, comfort with ambiguity, and the ability to bridge research and production seamlessly.
• Background at a frontier AI lab or on a post-training or evals team is a strong plus.
• Experience with reinforcement learning algorithm implementation, replay systems, or agent debugging tools is a plus.

Compensation & Benefits

Salary range: $100,000 to $200,000 USD annually. Visa sponsorship is not available.

Location

On-site role based primarily in Munich, Germany, with additional offices in Zurich and San Francisco. Remote arrangements may be discussed on a case-based basis.