CursorSoftware Engineer, ML Infrastructure
5mo ago
USASeniorgpuinfrastructuresoftware engineeringcomputescheduling
Build and support ML infrastructure for training agentic coding models with high-performance compute and software systems.
Responsibilities
- 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.
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
Other
- 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.
- 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