Engineering Manager, ML

Example projects include..

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 may be a fit if

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.