Engineering Manager, ML

Cursor1 weeks ago

Location

San Francisco

Type

Full Time

Salary

USD 185,000 – 280,000

Level

Manager

Role

Engineering Manager

Posted

Jul 9, 2026

Full TimeManager

The role

Summary

Lead a high-impact ML infrastructure team at Cursor, where you'll own the training, testing, and evaluation systems that power the world's leading AI-assisted coding platform. This role sits at the intersection of infrastructure and model behavior, requiring both strong technical leadership and hands-on systems expertise to build scalable ML training pipelines, robust evaluation frameworks, and optimized environments for model iteration at scale.

What you'll do

ML Infrastructure Leadership & Technical Direction: Set and execute technical strategy for model training, testing, and evaluation infrastructure at scale. Make critical architectural decisions that balance latency, quality, and cost tradeoffs. Own the codebase alongside your team, conducting technical reviews and debugging complex distributed systems issues that span both infrastructure and model behavior boundaries.

Reinforcement Learning & Evaluation Infrastructure: Design and implement rollout infrastructure enabling researchers to conduct large-scale RL experiments efficiently. Build evaluation pipelines that catch regressions before production deployment and provide rapid, trustworthy signal on experimental changes. Create reproducible, sandboxed training and testing environments optimized for iteration speed.

Team Leadership & Engineering Development: Source, interview, and hire exceptional infrastructure engineers aligned with Cursor's mission and values. Develop engineers through mentorship, code review, coaching, and strategic project assignments. Build a small, talent-dense team capable of operating with high autonomy in ambiguous environments while maintaining rigorous engineering standards.

Cross-Functional Collaboration & Communication: Partner closely with research teams to translate model-level requirements into concrete infrastructure solutions. Communicate fluently across both research and engineering domains, identifying technical ownership boundaries and ensuring clarity on whether issues stem from systems design or model behavior. Collaborate with product and research to prioritize infrastructure investments.

Measurement & Rigor: Establish rigorous measurement frameworks for infrastructure quality and team progress, especially in areas where shipping code doesn't guarantee impact. Instrument systems for observability, implement monitoring for reliability and performance under production load. Drive data-driven decision-making about infrastructure investments and prioritization.

What we look for

Technical

ML Infrastructure & Training FrameworksProduction experience building or leading infrastructure for model training, evaluation, or serving systems. Deep knowledge of distributed training frameworks, experiment tracking, model checkpoint management, and optimization for large-scale ML workloads.

Distributed Systems & Reliability EngineeringStrong fundamentals in distributed systems, including consistency, fault tolerance, and performance optimization. Proven ability to design systems that perform reliably under real production load, not just in theory. Experience with containerization, orchestration platforms, or microservices architecture.

Software Engineering ExcellenceDemonstrated ability to write clean, maintainable production code and conduct thorough technical code reviews. Comfortable with infrastructure-as-code principles, CI/CD pipelines, and modern development workflows. Proficiency in at least one systems-level programming language (e.g., Python, Go, Rust, C++).

Debugging Complex SystemsStrong debugging skills for production systems spanning multiple layers of abstraction. Ability to trace issues from application code through infrastructure to identify root causes. Experience with observability tools, profiling, and performance analysis.

Education

Computer Science or Related FieldBachelor's degree in Computer Science, Computer Engineering, Mathematics, or equivalent professional experience demonstrating advanced technical depth in systems design and software engineering.

Experience

ML Infrastructure LeadershipLed engineering teams building infrastructure for training, evaluating, or serving machine learning models in production environments. Track record of shipping infrastructure that unblocked research or production teams and improved iteration velocity.

Team Building & DevelopmentProven track record hiring and developing infrastructure engineers who have grown into senior roles. Experience mentoring engineers through both technical skill development and career growth. Demonstrated ability to build high-performing, autonomous teams.

Production Systems at ScaleHands-on experience owning or building production systems handling significant scale or complexity. Deep understanding of reliability, performance characteristics, and operational challenges in production environments.

Cross-Functional Technical CommunicationExperience translating between research and engineering domains, explaining technical tradeoffs and architectural decisions to non-infrastructure specialists. Ability to communicate with researchers fluently about model behavior and systems behavior.

Skills

Required skills

ML Model Training InfrastructureProduction experience with distributed training systems, experiment management, model checkpointing, and training pipeline orchestration for large-scale machine learning workloads.

Distributed Systems DesignCore expertise in designing and operating distributed systems including data consistency, fault tolerance, load balancing, and performance optimization under real-world constraints.

Infrastructure-as-Code & DevOpsProficiency with containerization (Docker), orchestration platforms, CI/CD systems, and infrastructure automation. Experience managing deployment pipelines and production infrastructure.

Technical LeadershipAbility to set technical direction, make architectural decisions, conduct thorough code reviews, and stay hands-on with implementation alongside the team.

Team Leadership & HiringExperience sourcing, interviewing, and hiring high-quality engineers. Track record of developing engineers through mentorship and strategic project assignments.

Nice to have

Reinforcement Learning InfrastructureHands-on experience building or maintaining infrastructure specifically for reinforcement learning training, including rollout systems, reward computation, and experiment orchestration at scale.

Evaluation & Testing FrameworksExperience designing evaluation pipelines for machine learning models, building regression detection systems, or creating comprehensive testing infrastructure for model behavior validation.

Simulated Environments & SandboxingExperience building and maintaining sandboxed or simulated environments for model training or testing, including reproducibility, isolation, and performance optimization.

AI/Coding Tools EcosystemFamiliarity with AI-assisted coding tools, language models, or code completion systems. Experience integrating with or building on top of modern AI development tools.

Observability & MonitoringDeep expertise in instrumentation, logging, tracing, and monitoring systems. Experience building observability platforms that provide actionable insights into system behavior and performance.

Cost Optimization for ML InfrastructureExperience optimizing infrastructure costs for machine learning systems, including compute resource allocation, efficient data movement, and cost-aware architectural decisions.

Compensation & benefits

Salary

USD 185,000 – 280,000 (annual)

Stock options

Available

Benefits

Equity & Ownership

Meaningful equity stake in Cursor as an early-stage company with significant growth potential in the AI developer tools space. Direct impact on the company's success.

Cutting-Edge Technology & Impact

Lead infrastructure powering the world's leading AI-assisted coding platform. Work at the intersection of distributed systems and machine learning on problems with significant real-world impact.

Small, Talent-Dense Team

Work in a flat organizational structure with exceptionally talented engineers and researchers. High autonomy, rapid decision-making, and direct impact on company direction.

Technical Leadership Opportunity

Rare opportunity to remain deeply technical while leading infrastructure strategy. Set architectural direction and stay hands-on with code and debugging alongside your team.

Mentorship & Growth

Develop and mentor exceptional infrastructure engineers. Build a team culture focused on learning, shipping, and creative problem-solving.

Interview process

1
Initial Screening Conversation — Preliminary call with Cursor's recruiting team to discuss your background in ML infrastructure and team leadership experience. Focus on your technical depth and experience scaling machine learning systems.
2
Technical Deep Dive — Conversation with current infrastructure team members or engineering leadership. Expect discussion of past projects, architectural decisions, debugging experiences, and how you've handled complex distributed systems challenges.
3
Leadership & Vision Interview — Discussion with Cursor's leadership about your approach to team building, technical direction-setting, and cross-functional collaboration. Emphasis on your vision for ML infrastructure at scale and how you'd approach ambiguity.
4
Research & Engineering Collaboration — Conversation with members of Cursor's research team to assess your ability to translate between research and engineering domains, understand model training dynamics, and make infrastructure tradeoff decisions.
5
Executive Alignment — Final conversation with senior leadership to discuss long-term vision, team scaling strategy, and alignment on Cursor's mission to automate coding through cutting-edge AI and infrastructure.