Software Engineer, ML Infrastructure

Cursor1 months ago

Location

SF / NY

Type

Full Time

Salary

USD 160,000 – 250,000

Level

Senior

Role

ML Infrastructure Engineer

Posted

Jan 27, 2026

Full TimeSenior

The role

Summary

Cursor is seeking a Software Engineer to join their ML Infrastructure team, working on large-scale compute, storage, and software infrastructure to support AI-powered coding model development. The role involves collaborating with ML researchers to build high-performance GPU clusters, improve training throughput, and develop systems for workload scheduling and data movement in both SF and NY offices.

What you'll do

ML Training Infrastructure: Collaborate with ML researchers to improve throughput and reliability of large-scale model training

GPU Cluster Management: Work with OEMs and cloud providers to plan and build cutting-edge GPU infrastructure

Compute Scalability: Improve density and scalability of compute environments for increasingly large RL workloads

Infrastructure Automation: Create software and systems to automate building, monitoring, and running GPU clusters

Workload Orchestration: Build workload scheduling and data movement systems to support growing training footprint

System Reliability: Ensure high availability and performance of distributed ML infrastructure

Cross-functional Collaboration: Work closely with ML engineers and researchers to enable their work through infrastructure improvements

What we look for

Technical

Systems ProgrammingStrong background in systems and infrastructure-focused software engineering

Programming LanguagesProficiency in Python, TypeScript, Rust, and Go for infrastructure development

Distributed SystemsExperience with distributed storage and networking infrastructure

Linux AdministrationDeep knowledge of Linux systems across cloud and bare metal environments

Large-scale InfrastructureExposure to systems managing thousands of nodes with significant resource footprints

Infrastructure as CodeProduction experience with IaC and configuration management across hosts and Kubernetes

Education

Computer Science DegreeBachelor's or Master's degree in Computer Science, Engineering, or related technical field

Systems Engineering BackgroundStrong foundation in computer systems, networking, and distributed computing

Experience

Infrastructure Engineering5+ years of experience in large-scale infrastructure development

Distributed SystemsProven experience building and maintaining distributed computing systems

ML InfrastructureExperience supporting machine learning workloads and training pipelines

Cloud and Bare MetalExperience with both cloud platforms and bare metal infrastructure management

Skills

Required skills

PythonAdvanced proficiency for ML infrastructure and automation

TypeScriptStrong skills for developer tooling and web interfaces

RustSystems programming for high-performance infrastructure

GoDistributed systems and cloud-native development

Linux SystemsDeep knowledge of Linux administration and networking

Distributed StorageExperience with large-scale storage systems

KubernetesContainer orchestration and cluster management

Infrastructure as CodeTerraform, Ansible, or similar tools for automation

Nice to have

NVIDIA GPU OperationsExperience with Blackwell and Hopper-class GPU hardware

InfiniBand/RoCEHigh-performance networking for GPU clusters

Ray FrameworkDistributed computing for ML workloads

SLURMWorkload management and job scheduling

ML Training PipelinesExperience optimizing machine learning training workflows

Bare Metal InfrastructureDirect hardware management and optimization

Compensation & benefits

Salary

USD 160,000 – 250,000 (annual)

Stock options

Available

Benefits

Equity Package

Significant equity stake in a fast-growing AI company

Health Insurance

Comprehensive medical, dental, and vision coverage

In-Person Culture

Cozy offices in North Beach SF and Manhattan with well-stocked libraries

Learning Resources

Access to cutting-edge research and development in AI/ML

Flat Organization

Direct impact and minimal bureaucracy in a talent-dense team

Professional Development

Opportunity to work with state-of-the-art ML infrastructure

Interview process

1
Initial Screen — Phone/video call with hiring manager to discuss background and role alignment
2
Technical Interview — Systems design and infrastructure architecture discussion with engineering team
3
Coding Assessment — Live coding session focused on systems programming and distributed systems
4
Cultural Fit — Conversation about values, working style, and team collaboration
5
Final Interview — Leadership interview covering career goals and company vision alignment
6
Reference Check — Verification of experience and performance with previous employers