Software Engineer, ML Infrastructure

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

Department: Engineering
Team: Machine Learning