Software Engineer, ML Infrastructure
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At a glance
Job overview
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. They seek strong engineers to develop high-performance infrastructure and software, collaborating with ML researchers to improve training frameworks, system reliability, performance, and developer experience.
Skills & qualifications
Skills
Full 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
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Collaborate with ML researchers to improve the throughput and reliability of training
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Work with OEMs, cloud service providers, and others to plan and build cutting-edge GPU infrastructure
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Improve the density and scalability of compute environments to enable increasingly large RL workloads
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Create software and systems to automate building, monitoring, and running GPU clusters
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Build workload scheduling and data movement systems to support Cursor’s growing training footprint
What we’re looking for
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A strong background in systems and infrastructure-focused software engineering, particularly in Python, Typescript, Rust, and Golang
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Experience with distributed storage and networking infrastructure, particularly on Linux systems across cloud and bare metal environments
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Exposure to large-scale systems and their unique challenges, ideally across thousands of nodes with significant resource footprints.
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Production use of infrastructure-as-code and configuration management, across hosts and Kubernetes
Nice to have
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Operational exposure to Nvidia GPUs with Infiniband or RoCE, particularly with Blackwell and Hopper-class hardware
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Exposure to Ray, Slurm, or other common compute and runtime schedulers
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