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ML Infrastructure Engineer

Cursor· ML Infrastructure
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Hybrid (Onsite/Remote not specified)Full-timePosted 7mo ago

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. The role collaborates with ML researchers and engineers to improve training frameworks, system reliability/performance, and developer experience.

Responsibilities

  • 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

Qualifications

  • 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

Skills

Required:PythonTypescriptRustGolangLinux
Last updated Oct 15, 2026. Interested? Apply on the company site.
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