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OpenAI

Software Engineer, DevOps

San Francisco (Remote)$177k–$327kfulltimemidAdded today

About this role

OpenAI seeks a DevOps engineer to design and build CI/CD systems for frontier AI models running on custom accelerators. You'll establish scalable infrastructure, automated testing pipelines, and GitOps practices that ensure correctness and performance across the hardware-software stack.

What you'll do

  • Define model CI strategy for frontier models on OpenAI custom silicon and accelerator systems
  • Build production CI/CD pipelines for regression testing, performance benchmarking, and release qualification
  • Design test matrices across models, hardware generations, and deployment environments
  • Establish GitOps practices for reproducible configuration, promotion, and environment consistency
  • Shape monorepo architecture, dependency management, and developer workflows at scale
  • Partner with model, compiler, kernel, and hardware teams to automate production gates and shorten feedback loops

What they're looking for

  • Large-scale CI/CD and developer infrastructure design
  • GitOps, release promotion, and reproducible deployment practices
  • Python, Go, Rust, or C++ for infrastructure automation
  • Monorepo architecture and build system design
  • Performance benchmarking and regression detection systems
  • Distributed systems, containers, schedulers, and cluster orchestration
  • Test automation and change-impact analysis
  • Observability and production engineering
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OpenAI

OpenAI builds AI infrastructure and products, including large-scale data center campuses for AI computing and generative AI applications for enterprise customers. The company is hiring civil engineers, project engineers, electrical design engineers, data center R&D engineers, and AI deployment engineers to expand its infrastructure capabilities and help customers deploy AI solutions.

View all jobs at OpenAI

Likely interview questions

  • Walk us through a large-scale CI/CD system you've built—what were the key design decisions and how did you measure success?
  • How would you design a performance benchmarking pipeline with stable baselines and actionable failure diagnosis?