OpenAI
AI Research and Deployment
SoftwareEngineer,BuildSystems/CI
Neural analysis suggests this role is
optimal for Senior candidates.
“Software Engineer, Build Systems / CI at OpenAI. Skills: Bazel, Buildkite, CI systems, Developer tooling. Own and evolve Bazel-based build and test workflows. Design and maintain Starlark rules, macros, toolchains, and integrations”
What You'll Achieve.
Make OpenAI one of the most productive engineering organizations; Preserve a high bar for correctness, reliability, and safety; Builds are fast; Tests are trusted; CI failures are understandable; Engineers can focus on shipping useful systems
Industry & Context.
Debug build failures; Debug distributed build and CI failures; CI failure analysis; Flaky test debugging
Participating in an on-call rotation
What They're Looking For.
Must Have
5+ years of software engineering experience, Significant experience building infrastructure or tooling for developers, Hands-on experience with Bazel, Buck, Pants, Gradle, or similar build systems, Understand tradeoffs of hermetic builds, dependency graphs, caching, sandboxing, and remote execution, Built or operated CI systems at scale, Comfortable writing production software for internal platforms, Debug distributed build and CI failures across source control, dependency management, containers, runners, remote caches, test frameworks, and service infrastructure, Communicate clearly across teams, Apply AI to developer infrastructure
Nice to Have
Experience in environments where build time, queue time, test flakiness, and developer trust materially affect engineering velocity, Pragmatic about platform adoption, Turn ambiguous productivity problems into concrete technical plans
What You'll Do.
Own and evolve Bazel-based build and test workflows
Design and maintain Starlark rules
Improve CI performance and reliability
Build systems to reduce unnecessary CI work
Improve local development workflows
Operate and optimize build infrastructure
Instrument build and CI systems
Own the reliability of systems
Participate in on-call rotation
How You'll Work.
Team & Collaboration
Partner directly with product, infrastructure, and research engineering teams; Understand pain points; Onboard projects; Debug hard build issues; Remove systemic bottlenecks; Communicate clearly across teams
Communication Scope
Communicate clearly across teams
Full Job Description
ABOUT THE ROLE The Engineering Acceleration team builds and operates the foundational systems that engineers use to build, test, and ship ChatGPT, the API, and OpenAI's infrastructure. We are looking for an engineer to help evolve OpenAI's build and continuous integration systems for a fast-growing engineering organization. This role sits at the intersection of developer productivity, build systems, distributed infrastructure, and software quality. You will work on the systems that determine how quickly and confidently engineers can move: Bazel-based builds, Buildkite pipelines, test selection, remote caching and execution, CI observability, and tooling that helps engineers understand and fix failures quickly. Our mission is to make OpenAI one of the most productive engineering organizations in the world while preserving a high bar for correctness, reliability, and safety. The best version of this work is invisible when it succeeds: builds are fast, tests are trusted, CI failures are understandable, and engineers can focus on shipping useful systems instead of fighting infrastructure. IN THIS ROLE, YOU WILL - Own and evolve Bazel-based build and test workflows across a large, polyglot monorepo. - Design and maintain Starlark rules, macros, toolchains, and integrations that make builds reproducible, hermetic, and easy for product teams to adopt. - Improve CI performance and reliability across Buildkite pipelines, including queue time, build time, cache hit rates, test sharding, retry behavior, and flake isolation. - Build systems that reduce unnecessary CI work through affected-target detection, dependency graph analysis, test selection, caching, batching, and smarter scheduling. - Improve local development workflows so engineers can reproduce CI behavior, debug build failures, and iterate quickly without learning every detail of the build stack. - Operate and optimize build infrastructure across Docker/OCI images, Kubernetes-based runners, cloud resources, and remot
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