# Software Engineer, ML Infrastructure

**Company:** [Cursor](https://hotfix.jobs/companies/cursor)
**Location:** San Francisco, CA, New York, NY
**Role:** ML Engineering
**Skills:** Python, TypeScript, Rust, Go, Kubernetes, Linux, Nvidia Gpus, InfiniBand, Ray, Slurm
**Posted:** 2026-01-27

> Builds large-scale ML infrastructure including GPU clusters, training frameworks, and workload schedulers. Requires strong systems engineering in Python, Rust, Golang, with Kubernetes and distributed systems experience.

## Job Description

## ML Infrastructure Engineer

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 might 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

## What we’re looking for
- 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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