Engineering Manager, ML
Lead a team building ML training, testing, and evaluation infrastructure at Cursor. Set technical direction, debug complex systems/model issues, partner with researchers on tradeoffs, and hire/grow engineers. Requires prior ML infra leadership, strong distributed systems skills, and desire to stay hands-on.
About the job
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.
Skills
Machine Learning, Distributed Systems, Infrastructure, Rl Training, Eval Frameworks, Python
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