ML Infrastructure Engineer

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 might 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

What we’re looking for

• 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