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OpenAI's Hardware organization is seeking a Software Engineer, DevOps to define and build CI/CD systems that qualify frontier models and the software stack running on OpenAI's custom AI accelerators. This role spans model workloads, accelerator software, systems infrastructure, and developer productivity.
You will establish scalable CI/CD, GitOps, and monorepo practices; design trustworthy regression and benchmarking pipelines; and partner with model, compiler, kernel, runtime, firmware, and hardware teams to shorten feedback loops without compromising production readiness.
Key responsibilities include:
- Define the model CI strategy for frontier models running on OpenAI custom silicon and integrated accelerator systems
- Build production CI/CD pipelines for functional regression, performance benchmarking, software stability, and release qualification
- Design representative test matrices across models, configurations, hardware generations, software components, and deployment environments
- Create reliable performance baselines, regression detection, bisect and triage workflows, and clear ownership for failures
- Establish GitOps practices for reproducible configuration, promotion, rollback, auditability, and environment consistency
- Shape monorepo architecture, dependency management, build and test boundaries, change validation, and developer workflows at scale
- Develop scalable orchestration, artifact management, caching, scheduling, observability, and capacity controls for accelerator-backed CI
- Partner with model, compiler, kernel, runtime, firmware, validation, and hardware teams to translate release risks into automated gates
- Improve CI reliability, speed, debuggability, and cost efficiency while maintaining high confidence in production software
Requirements:
- Experience building or operating large-scale CI/CD, developer infrastructure, test automation, or production engineering systems
- Understanding of modern software delivery practices, including GitOps, release promotion, rollback, reproducibility, and policy-driven automation
- Experience designing monorepos, build systems, dependency graphs, test selection, or change-impact analysis
- Ability to design regression and performance-benchmarking systems with stable baselines, useful metrics, and actionable failure diagnosis
- Proficiency in Python, Go, Rust, C++, or another language used to build reliable infrastructure and automation
- Experience with distributed systems, schedulers, containers, clusters, or heterogeneous compute infrastructure
- Ability to collaborate with model and systems engineers to turn complex accelerator workloads into repeatable production qualification
- Strong commitment to reliability, observability, developer experience, and reducing time from code change to trustworthy signal
Note: Candidates may need to meet certain legal status requirements to comply with U.S. export control laws and regulations.
