Software Engineer, ML Infrastructure

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Наша компания

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.

О роли

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

Чем предстоит заниматься

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

Наши требования

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

Дополнительно

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

Cursor

San Francisco

ГрейдMiddle

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Опубликовано27 янв.

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