Engineering Manager, ML

Responsibilities

• Lead a team of engineers building infrastructure to train, test, and evaluate ML models.
• Set technical direction for training and evaluating models at scale.
• Debug issues where it's unclear if the root cause is a systems bug or model behavior.
• Design rollout infrastructure for RL experiments at scale.
• Build eval pipelines to catch regressions and provide fast feedback on changes.
• Own training and testing environments that are sandboxed, reproducible, and optimized for iteration speed.
• Bring rigor to measuring quality and progress for models.
• Partner with research teams to translate model tradeoffs (latency, quality, cost) into infrastructure decisions.
• Hire and grow the team through sourcing, interviewing, coaching, mentorship, and project assignments.

Requirements

• Led engineering teams building infrastructure for training, evaluating, or serving ML models in production.
• Strong infrastructure and distributed systems fundamentals, with understanding of reliability and performance under real load.
• Desire to stay technical: comfortable writing code and reviewing PRs in depth.
• Comfortable operating in ambiguity, asking right questions, and making decisions with incomplete information.
• Track record of hiring and developing strong engineers.
• Ability to communicate fluently with researchers on model behavior and engineers on systems design.

Nice-to-Haves

• Hands-on experience with RL training infrastructure.
• Experience with eval frameworks.
• Experience building and maintaining simulated environments for model training or testing.