Cursor
Software Engineer, ML Platform
About this role
Build ML infrastructure and platform systems that enable researchers to move quickly on large GPU fleets and turn product usage into better models. You'll own core distributed systems used by ML researchers and product engineers, working across telemetry, data pipelines, observability, and ML DevX in a flat, high-ownership environment.
What you'll do
- Design and operate distributed platform systems for ML researchers and product engineers
- Build telemetry, data pipelines, or scheduling infrastructure depending on team assignment
- Partner with research teams to convert pain points into durable infrastructure solutions
- Own reliability, performance, and developer experience for assigned systems
- Ship iteratively while measuring impact and raising quality standards
- Maintain production systems at scale across cloud and GPU clusters
What they're looking for
- Distributed systems and infrastructure engineering
- Production systems at scale (ingestion, pipelines, scheduling, orchestration)
- Kubernetes, Ray, or equivalent orchestration platforms
- Linux, cloud infrastructure, and bare metal operations
- Data platform or ML training infrastructure experience
- Event ingestion, analytics pipelines, or tracing systems
- Collaboration with ML researchers and product teams
- High-ownership, iterative shipping mindset
Benefits
- Work in flat, talent-dense organization with creative, truth-seeking team
- High ownership and short feedback loops on impactful work
- In-person offices in San Francisco (North Beach, Palo Alto) and Manhattan with well-stocked libraries
- Opportunity to influence ML research velocity through platform work
- Collaborate closely with cutting-edge ML research
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Cursor
Cursor builds an AI-driven code editor used by millions of developers to transform how software is built. The company is hiring for infrastructure engineers, ML systems specialists, enterprise platform builders, security engineers, and customer success roles focused on driving adoption within large organizations.
- Website
- cursor.com
Likely interview questions
- Walk us through a production distributed system you've owned at scale—what were the hardest reliability or performance challenges?
- Describe your experience with orchestration platforms like Kubernetes or Ray and how you've used them to solve infrastructure problems.