EMT3_VM_Gen_AI_ML_Senior_Developer
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You'll own machine learning from day one at an early-stage AI startup focused on post-training, agent systems, and research infrastructure. Day-to-day work includes building training data pipelines, designing evaluation frameworks, constructing agent environments with observability systems, and setting ML roadmap priorities. You need 3+ years delivering production ML systems, hands-on experience with post-training pipelines and model evals, strong Python skills, and deep knowledge of trajectory data and reinforcement learning. The role is based in Munich, Germany, on-site with possible case-by-case remote discussion, offering $100,000–$200,000 annually.
Written from this posting by Neural Jobs AI. The full description is below.
This is a founding ML engineering role at an early-stage AI startup, sitting at the intersection of post-training, agent systems, and research infrastructure. You will own the ML function from day one, working closely with the founding team to define technical direction, run experiments, and build the systems that power the next generation of AI agents.
Structure, filter, and score experimental trajectories to build high-quality training data pipelines.
Design and implement evaluation frameworks and benchmarks that measure model reasoning, planning, and 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 as the company scales.
3 or more 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.
Direct experience designing and implementing evals or benchmarks for ML models.
Strong Python skills and comfort with systems-level programming for ML infrastructure.
Deep understanding of trajectory data, reward modeling, and agent decision-making systems.
Experience with reinforcement learning algorithm implementation and tuning.
Experience building replay systems, debugging tools, and observability systems for agent trajectories.
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.
Salary range: $100,000 to $200,000 USD annually. Visa sponsorship is not available.
Primary location is Munich, Germany, with additional offices in Zurich and San Francisco. This is an on-site role; remote arrangements may be discussed on a case-by-case basis.
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