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 roleThe 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 doCollaborate 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
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
Skills Required
- Systems and infrastructure-focused software engineering experience, particularly in Python, Typescript, Rust, and Golang
- Experience with distributed storage and networking on Linux across cloud and bare-metal environments
- Experience with large-scale systems (scaling to hundreds/thousands of nodes) and high-resource workloads
- Production use of infrastructure-as-code and configuration management across hosts and Kubernetes
- Experience building, automating, monitoring, and running GPU clusters and workload scheduling/data movement systems
- Operational exposure to Nvidia GPUs with Infiniband or RoCE, especially Blackwell or Hopper-class hardware
- Exposure to Ray, Slurm, or other compute/runtime schedulers
What We Do
We'd like to automate coding. To advance that mission, we're building Cursor. Our work includes training the world’s most widely used coding models, creating infrastructure that supports billions of requests per day, and building better ways for humans and AIs to work together.
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