Anysphere에서 ML 인프라 팀을 이끌 Engineering Manager를 채용합니다. ML 모델 학습 및 평가 인프라 구축을 주도하며, 연구원과 협업하여 모델 성능과 시스템 효율성을 최적화합니다. 분산 시스템에 대한 깊은 이해와 엔지니어링 팀 리딩 경험이 필수입니다. 기술적 깊이를 유지하며 팀의 성장을 견인할 역량 있는 분을 찾습니다.
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.
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.
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.
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.
