Role at a glance
- Job function
-
Software Engineering & IT Software Engineering
- Salary
- Not Disclosed
- Location
- San Francisco, United States
- Work arrangement
- Hybrid
- Employment
- Full-time
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Role Summary
The ChatGPT Velocity team is hiring an infrastructure engineer to improve the speed and reliability of ChatGPT’s development-to-production workflow. The role spans local development, build systems, CI, deployment infrastructure, testing, and production safety signals.
What You'll Do
- Reduce local development startup and iteration latency for ChatGPT engineers.
- Improve Bazel and build performance, including cache effectiveness, reproducibility, disk usage, remote cache behavior, and invalidation...
- Improve CI reliability and merge throughput by addressing unrelated failures, tail latency, flaky or mis-owned tests, quarantine...
- Build agents to help PRs progress through rebasing, conflict resolution, CI triage, safe reruns, owner routing, and identification of...
- Make deploy pipelines more self-healing and improve deploy observability so engineers can understand change status, blockers, signal...
- Establish lightweight before-and-after impact metrics, such as commit-to-prod latency, deploy throughput, CI tail latency, local bootup...
Generated from the employer's posting. Verify important details before applying.
View full postingQualifications
Strong experience with large-scale developer infrastructure, build systems, CI/CD, deployment systems, or production reliability; ability to debug across local and distributed systems; comfort with Bazel, Buildkite, Kubernetes, Temporal, Python/TypeScript services, GitHub workflows, and monorepo tooling, or ability to ramp quickly on equivalents. The posting also emphasizes developer-centered workflow design, safe automation, cross-team communication, and practical measurement.
Required
- Strong experience with large-scale developer infrastructure, build systems, CI/CD, deployment systems, or production reliability.
- Ability to debug across local machines, devboxes, remote caches, monorepos, service dependencies, auth systems, and distributed deploy...
- Comfort with systems such as Bazel, Buildkite, Kubernetes, Temporal, Python/TypeScript services, GitHub workflows, and large monorepo...
- Care about whether developers can understand and trust workflows, not only whether a subsystem is technically healthy.
- Ability to replace repeated manual intervention with policy-backed automation while preserving production safety.
- Strong cross-team communication and ability to turn ambiguous reports into owners, hypotheses, metrics, and shipped fixes.
- Preference for practical measurement and attentiveness to developer frustration as a signal of systems problems.
Preferred
- Experience improving developer experience in a very large monorepo.
- Experience with remote execution, remote caching, hermetic builds, or cache reproducibility.
- Experience designing CI quarantine, test ownership, merge queue, or auto-revert systems.
- Experience with progressive delivery, canary/preview/stable deploy pipelines, synthetics, alert quality, and deploy-manager style workflows.
- Experience building agent-assisted developer workflows, such as automated PR babysitting, CI triage, or conflict resolution.
- Familiarity with security and access-control constraints around secrets, internal auth, VPN/exit-node networking, and local development.
Original job description
Content provided by the employer
Original job description
Content provided by the employer
About the Team
The ChatGPT Velocity team owns the health of the end-to-end developer and deploy loop for ChatGPT-facing services. Our mission is to help ChatGPT engineers ship confidently with a fast, lightweight development cycle and a trusted path to production.
ChatGPT development depends on a long chain: local environments, Bazel builds, CI, tests, merges, release candidates, deploys, synthetics, alerts, and production health. When any link is slow, flaky, or unclear, engineers lose hours, fixes reach users later, and on-call load rises. This team turns that cross-cutting drag into owned, measured, and durable improvements.
About the Role
We are hiring an experienced infrastructure engineer to make ChatGPT development dramatically faster and more reliable. You will work across local development, build systems, CI, deployment infrastructure, test ownership, and production safety signals to reduce the time and attention required to move a healthy change from development to production.
This is not a narrow tooling role. The best person for this job will be comfortable diagnosing messy distributed systems, reading developer pain signals from Slack and metrics, building reliable automation, negotiating ownership across teams, and making tradeoffs between velocity and production safety. You should enjoy treating engineers as users and turning confusing workflow failures into clear, measured systems.
In this role, you will:
Reduce local development startup and iteration latency for ChatGPT engineers
Improve Bazel and build performance for local machines, devboxes, and CI, including cache effectiveness, reproducibility, disk usage, remote cache behavior, and observability into invalidations.
Improve CI reliability and merge throughput by reducing unrelated failures, tail latency, flaky or mis-owned tests, quarantine friction, and repeated manual reruns.
Build agents that help PRs make forward progress: rebasing, conflict resolution, CI triage, safe reruns, owner routing, and clear identification of external blockers.
Make deploy pipelines more self-healing, especially when transient alerts, known-bad clusters, recovered synthetics, stale RCs, or noisy gates should not require human babysitting.
Improve deploy observability so engineers can quickly tell where a change is, what is blocking it, whether the signal is trustworthy, and who owns the next action.
Establish lightweight metrics for before-and-after impact, such as commit-to-prod latency, deploy-window throughput, CI tail latency, local bootup time, failure rates, manual interventions, false-positive gates, and developer-reported pain.
You might thrive in this role if you:
Have strong experience with large-scale developer infrastructure, build systems, CI/CD, deployment systems, or production reliability.
Can debug across local machines, devboxes, remote caches, monorepos, service dependencies, auth systems, and distributed deploy pipelines.
Are comfortable with systems like Bazel, Buildkite, Kubernetes, Temporal, Python/TypeScript services, GitHub workflows, and large monorepo tooling, or can ramp quickly on equivalents.
Think in product loops as much as systems loops: you care whether developers can understand and trust the workflow, not just whether a subsystem is technically healthy.
Know how to replace repeated manual intervention with policy-backed automation while preserving production safety.
Communicate well across teams and can turn ambiguous "this is slow/broken/flaky" reports into crisp owners, hypotheses, metrics, and shipped fixes.
Prefer practical measurement over vibes, while still knowing that developer frustration is often the first signal of a real systems problem.
Are energized by high-leverage work where a small improvement can save hundreds of engineering hours.
Nice to have:
Experience improving developer experience in a very large monorepo.
Experience with remote execution, remote caching, hermetic builds, or cache reproducibility.
Experience designing CI quarantine, test ownership, merge queue, or auto-revert systems.
Experience with progressive delivery, canary/preview/stable deploy pipelines, synthetics, alert quality, and deploy-manager style workflows.
Experience building agent-assisted developer workflows, such as automated PR babysitting, CI triage, or conflict resolution.
Familiarity with security and access-control constraints around secrets, internal auth, VPN/exit-node networking, and local development.
About OpenAI
OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of humanity.
We are an equal opportunity employer, and we do not discriminate on the basis of race, religion, color, national origin, sex, sexual orientation, age, veteran status, disability, genetic information, or other applicable legally protected characteristic.
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Background checks for applicants will be administered in accordance with applicable law, and qualified applicants with arrest or conviction records will be considered for employment consistent with those laws, including the San Francisco Fair Chance Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act, for US-based candidates. For unincorporated Los Angeles County workers: we reasonably believe that criminal history may have a direct, adverse and negative relationship with the following job duties, potentially resulting in the withdrawal of a conditional offer of employment: protect computer hardware entrusted to you from theft, loss or damage; return all computer hardware in your possession (including the data contained therein) upon termination of employment or end of assignment; and maintain the confidentiality of proprietary, confidential, and non-public information. In addition, job duties require access to secure and protected information technology systems and related data security obligations.
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About the company
OpenAI
Large Enterprise
OpenAI is an artificial intelligence research organization dedicated to advancing digital intelligence in a way that is safe and beneficial for humanity. Founded in December 2015, it focuses on developing cutting-edge AI technologies and models, such as the widely recognized language model GPT-3. OpenAI aims to promote and develop friendly AI that aligns with human values and addresses global challenges, making significant contributions to a variety of fields including education, healthcare, and robotics. By collaborating with various stakeholders, OpenAI seeks to ensure that the benefits of AI are accessible to all.