Software Engineer, ML Infrastructure

at Cursor · Coding AI · San Francisco, CA · Engineering

Software Engineer focused on building and scaling ML infrastructure, including compute, storage, and software systems to support large-scale training of agentic coding models. The role involves collaborating with researchers, managing GPU infrastructure, and improving training framework performance and reliability.

What you'd actually do

  1. Collaborate with ML researchers to improve the throughput and reliability of training
  2. Work with OEMs, cloud service providers, and others to plan and build cutting-edge GPU infrastructure
  3. Improve the density and scalability of compute environments to enable increasingly large RL workloads
  4. Create software and systems to automate building, monitoring, and running GPU clusters
  5. Build workload scheduling and data movement systems to support Cursor’s growing training footprint

Skills

Required

  • Systems and infrastructure-focused software engineering
  • Python
  • Typescript
  • Rust
  • Golang
  • Distributed storage and networking infrastructure
  • Linux systems
  • Cloud and bare metal environments
  • Large-scale systems
  • Infrastructure-as-code
  • Configuration management
  • Kubernetes

Nice to have

  • Nvidia GPUs with Infiniband or RoCE
  • Blackwell and Hopper-class hardware
  • Ray
  • Slurm
  • Compute and runtime schedulers

What the JD emphasized

  • large-scale compute
  • GPU infrastructure
  • training framework
  • RL workloads
  • thousands of nodes

Other signals

  • ML Infrastructure
  • large-scale compute
  • GPU infrastructure
  • training framework
  • RL workloads
Read full job description

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

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