About the role
The ML Infrastructure team at Cursor builds large-scale compute, storage, and software infrastructure to support the company'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
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
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