Software Engineer, ML Infrastructure
Cursor · On-site
MeritLog read this listing from Cursor's Ashby job board and last checked it on September 12, 2026.
Source: the employer's Ashby job board. Open the original listing for current details.
Job details
- Work model
- On-site
- Salary
- Not listed by source
- Location
- San Francisco
- Occupation
- Software Developers(O*NET 15-1252.00)
- Company website
- cursor.com
Hiring context
How this role compares at Cursor
Cursor has 127 live roles in MeritLog’s catalog across 8 job families, and 8 of them are in data & analytics. 0 of those listings publish a pay range, a disclosure rate of 0%.
Cursor concentrates this hiring in:
Counted across the job boards MeritLog tracks, at the time this page was served. Pay comparisons use only listings that publish a complete range in the same currency and period.
What the role asks for
What you'd 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
- 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
What they're asking for
- Operational exposure to Nvidia GPUs with Infiniband or RoCE, particularly with Blackwell and Hopper-class hardwareSkillPreferred
- Exposure to Ray, Slurm, or other common compute and runtime schedulersSkillPreferred
Parsed by MeritLog from the employer’s own posting. The full description follows below.
Job description
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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