Careers

Software Engineer, ML Infrastructure

Engineering · Full-time · San Francisco; New York

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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.

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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