Founding Machine Learning Engineer

Wwshemi; München; Hybrid; EUR 90.000 - 224.000; Vollzeit

Zusammenfassung

Wwshemi in Munich is seeking a lead ML engineer to own the machine learning function from research to production. You will shape post-training and agent systems, define priorities, and build the foundations for a growing ML team.

This on-site role requires hands-on experience with production ML, post-training data pipelines, and strong Python/systems skills to bridge research and production in a fast-moving environment. VISA sponsorship is not available.

Qualifikationen

• 3+ years of professional experience delivering production ML systems.
• Hands-on experience with post-training ML and data pipelines including structuring, filtering, and scoring training data.
• Strong Python and systems-level software engineering 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 in a fast-moving environment.

Aufgaben

• 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.

Kenntnisse

Production ML

Python

Reinforcement learning

ML infrastructure

Data pipelines

Experiment design

Jobbeschreibung

This listing's locations On-site - Munich, Bavaria, Germany

Employment Full-time

Workplace On site

Compensation USD 100000-250000 per year

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.

Salary range: $100,000 to $250,000 USD annually. Visa sponsorship is not available.