Engineering Manager, ML Infrastructure & Systems

Cursor; San Francisco (CA); On-site; USD 180,000 - 260,000; Full time

Job summary

Cursor is seeking a Senior Infrastructure Engineering Manager to lead a team building the core infrastructure used to train, test, and evaluate ML models at scale. You will steer technical direction, debug with engineers, and collaborate closely with researchers to balance latency, quality, and cost.

The role spans RL experiments rollout, evaluation pipelines, and environment ownership, with a focus on growing engineers through mentorship.

Qualifications

• Led engineering teams building infrastructure for ML models in production.
• Strong infrastructure and distributed systems fundamentals under real load.
• Hands-on coding and thorough PR reviews.
• Comfortable operating in ambiguity and making decisions with incomplete information.
• Proven track record of hiring and developing engineers.
• Ability to communicate with researchers and engineers about system design and model behavior.
• Bonus: experience with RL training infrastructure or eval frameworks.

Responsibilities

• Set technical direction for training and evaluating models at scale.
• Debug alongside your team and work daily with researchers to turn tradeoffs into infrastructure.
• Own rollout infrastructure for RL experiments at scale.
• Design eval pipelines that catch regressions before they ship and provide fast signals.
• Own sandboxed, reproducible environments for model training and testing.
• Partner with researchers to translate model-level tradeoffs into concrete infra decisions.
• Hire and grow the team: sourcing, interviewing, and mentoring engineers.

Skills

Infrastructure

Distributed systems

Coding

PR reviews

Team leadership

Hiring & mentoring

Research collaboration

Job description

Cursor is seeking a Senior Infrastructure Engineering Manager to lead a team building the core infrastructure used to train, test, and evaluate ML models at scale. You will steer technical direction, debug with engineers, and collaborate closely with researchers to balance latency, quality, and cost.

The role spans RL experiments rollout, evaluation pipelines, and environment ownership, with a focus on growing engineers through mentorship.