Software Engineer, ML Infrastructure
cursor Open · verified Oct 22, 2026 At a glance
Mid-level ML Platform & Ops role at cursor. San Francisco · full-time · no salary stated · we estimate $209,400–$315,250.
cursor has not published a salary for this role. The figure below is our estimate, not theirs.
aimlroles summary, based on the employer's posting.
What you'll do
- Work with ML researchers to make training run faster and more reliably
- Plan GPU infrastructure with hardware manufacturers, cloud providers and other partners
- Increase compute density and scale to handle larger reinforcement-learning workloads
- Build software to automate GPU cluster setup, monitoring and operation
- Develop scheduling and data-transfer systems for the expanding training workload
What you bring
- You have a strong systems software background, especially with Python, TypeScript, Rust or Go
- You have worked with distributed storage and networking on Linux, in cloud or bare-metal environments
- You have handled large-scale systems and used infrastructure-as-code and configuration tools across hosts and Kubernetes
Who this fits
This mid-level, on-site San Francisco role suits a software engineer focused on systems and infrastructure who works closely with ML researchers. Experience with Nvidia GPU operations or compute schedulers such as Ray and Slurm is a plus.
From the employer
SpaceX was founded under the belief that a future where humanity is out exploring the stars is fundamentally more exciting than one where we are not. Today, SpaceX is actively developing the technologies to make this possible, with the ultimate goal of making life multiplanetary and expanding consciousness to the stars.
SpaceXSI is building Super Intelligence to help humanity understand the universe. Our teams are building the full Super Intelligence stack: from our Colossus supercomputers, to Grok models, to our suite of applications, including Grok Bot, Grok Build, Grok Imagine, Grok Voice, and X. We're creating a powerful flywheel where more compute trains better models, better models power more useful products, and what we learn from people using those products shapes what we build next.
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