Cursor

Software Engineer, ML Infrastructure

San Francisco, CA, United States · On-site · FullTime

Posted over 6 months ago: check it’s still open

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About the role

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EmployerCursor
LocationSan Francisco, CA, United States
Work styleOn-site
Employment typeFullTime
Employer posted8 months ago

Our mission is to automate coding. The first step in our journey is to build the best tool for professional programmers, using a combination of inventive research, design, and engineering. Our organization is very flat, and our team is small and talent dense. We particularly like people who are truth-seeking, passionate, and creative. We enjoy spirited debate, crazy ideas, and shipping code.

About the role

The ML Infrastructure team builds large-scale compute, storage, and software infrastructure to support Cursor’s work building the world’s best agentic coding model. We’re looking for strong engineers who are interested in building high-performance infrastructure and the software to support it. This role works closely with ML researchers and engineers to enable their work through improvements to our training framework, systems reliability/performance, and developer experience.

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What you’ll do

Collaborate with ML researchers to improve the throughput and reliability of training

Work with OEMs, cloud service providers, and others to plan and build cutting-edge GPU infrastructure

Improve the density and scalability of compute environments to enable increasingly large RL workloads

Create software and systems to automate building, monitoring, and running GPU clusters

Build workload scheduling and data movement systems to support Cursor’s growing training footprint

You may be a fit if

A strong background in systems and infrastructure-focused software engineering, particularly in Python, Typescript, Rust, and Golang

Experience with distributed storage and networking infrastructure, particularly on Linux systems across cloud and bare metal environments

Exposure to large-scale systems and their unique challenges, ideally across thousands of nodes with significant resource footprints.

Production use of infrastructure-as-code and configuration management, across hosts and Kubernetes

Nice to have

Operational exposure to Nvidia GPUs with Infiniband or RoCE, particularly with Blackwell and Hopper-class hardware

Exposure to Ray, Slurm, or other common compute and runtime schedulers

#LI-DNI

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