Engineering Manager, ML

Responsibilities

• ▹Set technical direction for how models are trained and evaluated at scale
• ▹Build rollout infrastructure that lets researchers run RL experiments at scale without fighting the plumbing
• ▹Design eval pipelines that catch regressions before they ship and give researchers fast, trustworthy signal
• ▹Own the environments in which models are trained and tested: sandboxed, reproducible, and fast enough that iteration speed isn't the bottleneck
• ▹Bring rigor to how the team measures quality and progress
• ▹Partner with research to translate model-level tradeoffs (latency, quality, cost) into concrete infrastructure decisions
• ▹Hire and grow the team: sourcing, interviewing, and closing exceptional infrastructure engineers, with coaching and mentorship

Requirements

• ▹Led engineering teams building infrastructure that trains, evaluates, or serves ML models in production
• ▹Strong infrastructure and distributed systems fundamentals, know what reliability and performance look like under real load
• ▹Stay technical: comfortable writing code, reviewing PRs with depth, and using tools like Cursor itself
• ▹Comfortable operating in ambiguity: ask the right questions and make sound decisions with incomplete information
• ▹Track record of hiring and developing engineers who are better than you were at their stage
• ▹Talk fluently with researchers about model behavior and with engineers about systems design

Nice to have

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

Soft skills

About the company

Cursor's mission is to automate coding. They're building the best tool for professional programmers by combining inventive research, design, and engineering. The organization is very flat, with a small, talent-dense team that values people who are truth-seeking, passionate, and creative.