Engineering Manager, ML

Cursor; New York (NY); On-site; USD 180,000 - 260,000; Full time

Job summary

Cursor is seeking a senior engineering leader to helm the infrastructure that trains, tests, and evaluates our ML models in production. You will guide a team of engineers, debug across systems and model behavior, and shape the technical direction for scalable, low-latency pipelines.

This role blends deep systems work with research collaboration, and offers opportunities to own critical infra decisions end-to-end.

Qualifications

• Led engineering teams building ML training and evaluation infra.
• Strong distributed systems fundamentals and reliability.
• Hands-on coding and ability to review PRs.

Responsibilities

• Lead infra for ML model training, testing, and evaluation at scale.
• Collaborate with researchers to translate model tradeoffs into scalable pipelines.

Skills

Infrastructure leadership

Distributed systems

ML infrastructure

Hiring & mentorship

Job description

Our mission is to automate coding. The first step in our journey is to build the best tool for professional programmers, using a combination of inventive research, design, and engineering. Our organization is very flat, and our team is small and talent dense. We particularly like people who are truth‑seeking, passionate, and creative. We enjoy spirited debate, crazy ideas, and shipping code.

About the Role

You will lead a team of engineers building the infrastructure used to train, test, and evaluate our models. This is one of the few places at Cursor where infrastructure and model behavior meet directly: when something breaks, it’s rarely obvious whether it’s a systems bug or the model doing exactly what it was trained to do, and your team has to be good at telling the difference before they can fix it.

You’ll set technical direction for how we train and evaluate models at scale, stay close enough to the code to debug alongside your team, and work daily with researchers to turn tradeoffs in latency, quality, and cost into infrastructure that actually gets built. We’re hiring across a range of scope for this role, depending on experience and the size of problem you’re ready to own.

Example Projects

• Building the rollout infrastructure that lets researchers run RL experiments at scale without fighting the plumbing.
• Designing eval pipelines that catch regressions before they ship, and give researchers fast, trustworthy signal on whether a change actually helped.
• Owning the environments in which models are trained and tested: sandboxed, reproducible, and fast enough that iteration speed isn’t the bottleneck.
• Bringing rigor to how the team measures quality and progress, in places where “did it ship” isn’t the same as “did it work?”
• Partnering with research to translate model‑level tradeoffs (latency, quality, cost) into concrete infrastructure decisions.
• Hiring and growing the team: sourcing, interviewing, and closing exceptional infrastructure engineers, while developing your engineers through coaching, mentorship, and high‑leverage project assignments.

Fit Criteria

• You’ve led engineering teams building infrastructure that trains, evaluates, or serves ML models in production.
• You have strong infrastructure and distributed systems fundamentals: you know what reliability and performance look like under real load, not just in a design doc.
• You genuinely want to stay technical: you’re comfortable writing code, reviewing PRs with depth, and using tools like Cursor itself to move fast.
• You’re comfortable operating in ambiguity: you ask the right questions, make sound decisions with incomplete information, and help the team find a path forward.
• You have a track record of hiring and developing engineers who are better than you were at their stage.
• You can talk fluently with researchers about model behavior and with engineers about systems design, and you know when a problem is actually the other team’s.
• Bonus: hands‑on experience with RL training infrastructure, eval frameworks, or building and maintaining simulated environments for model training or testing.