Software Engineer, ML Infrastructure

About the Company

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.

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

Requirements

• Strong background in systems and infrastructure-focused software engineering
• Proficiency in Python, Typescript, Rust, or Golang
• Experience with distributed storage and networking infrastructure on Linux systems
• Experience with cloud and bare metal environments
• Exposure to large-scale systems across thousands of nodes
• Production use of infrastructure-as-code and configuration management
• Experience with Kubernetes

Preferred Qualifications

• Operational exposure to Nvidia GPUs with Infiniband or RoCE
• Experience with Blackwell and Hopper-class hardware
• Exposure to Ray, Slurm, or other common compute and runtime schedulers