Engineering Manager, ML

Cursor Cursor · Coding AI · San Francisco, CA · Engineering

Engineering Manager to lead a team building ML infrastructure for training, testing, and evaluating models. The role involves setting technical direction, debugging systems and models, and collaborating with researchers on infrastructure decisions. Responsibilities include building rollout infrastructure for RL experiments, designing eval pipelines, owning training/testing environments, measuring quality, and hiring/growing the team.

What you'd actually do

  1. You will lead a team of engineers building the infrastructure used to train, test, and evaluate our models.
  2. 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.
  3. Building the rollout infrastructure that lets researchers run RL experiments at scale without fighting the plumbing.
  4. Designing eval pipelines that catch regressions before they ship, and give researchers fast, trustworthy signal on whether a change actually helped.
  5. Hiring and growing the team: sourcing, interviewing, and closing exceptional infrastructure engineers, while developing your engineers through coaching, mentorship, and high-leverage project assignments.

Skills

Required

  • Lead engineering teams building ML infrastructure
  • ML model training infrastructure
  • ML model evaluation infrastructure
  • ML model serving infrastructure
  • Infrastructure and distributed systems fundamentals
  • Reliability and performance under load
  • Writing code and code reviews
  • Operating in ambiguity
  • Hiring and developing engineers
  • Communication with researchers and engineers
  • Debugging systems and models

Nice to have

  • RL training infrastructure
  • Eval frameworks
  • Building and maintaining simulated environments for model training or testing

What the JD emphasized

  • infrastructure and model behavior meet directly
  • telling the difference
  • train, test, and evaluate our models
  • train and evaluate models at scale
  • debug alongside your team
  • turn tradeoffs in latency, quality, and cost into infrastructure
  • trains, evaluates, or serves ML models in production
  • reliability and performance under real load
  • stay technical
  • operating in ambiguity
  • hiring and developing engineers
  • talk fluently with researchers about model behavior and with engineers about systems design
  • hands-on experience with RL training infrastructure, eval frameworks, or building and maintaining simulated environments for model training or testing

Other signals

  • ML infrastructure
  • model training
  • model evaluation
  • engineering management