OpenAI· Applied AI Engineering· San Francisco
Software Engineer, ChatGPT Infrastructure
Comp$255K – $405K
Classified Tasks (17)
Automate 0%Augment 88%Human-Only 12%
Augment (15)
AI assists, human decides
1. Design and implement infrastructure platforms and shared systems used by many engineers and services.
technical
2. Operate and maintain production infrastructure platforms to meet performance and reliability targets.
operational
3. Define interfaces and design clean APIs and enforceable contracts to reduce coupling across services.
technical
4. Develop core abstractions, libraries, and service frameworks that standardize system building and integration.
technical
5. Create developer tooling and golden paths for common workflows (data access, service integration, request lifecycle) to accelerate safe iteration.
technical
6. Translate real-world production constraints into simple, enforceable abstractions and safe defaults.
technical
7. Design and implement scalability and performance primitives to reduce tail latency, increase throughput, and control costs.
technical
8. Implement reliability guardrails, including rate limiting, load shedding, dependency isolation, backpressure, safe fallbacks, and regression controls.
technical
9. Build deployment and rollout systems that support progressive delivery, automated verification, and fast rollback.
technical
10. Design and build observability systems including instrumentation, metrics models, and investigative tooling to enable precise diagnostics.
technical
11. Drive improvements in reliability and performance through principled design, measurement, and iterative hardening.
operational
14. Create tooling and processes that prevent regressions during development and deployment.
technical
15. Incorporate operational lessons and defaults into platform primitives and golden paths.
operational
16. Design data paths and rollout mechanisms that enable teams to ship changes at scale.
technical
17. Standardize service contracts and integration patterns to enable independent evolution across the ecosystem.
technical
Human-Only (2)
Requires human judgment
12. Partner with engineering and product teams to identify systemic pain points and develop reusable platform solutions.
leadership
13. Own end-to-end outcomes for platform features, including design, implementation, rollout, and operational maturity.
leadership
Job description
Software Engineer, ChatGPT Infrastructure | OpenAI Careers ## Software Engineer, ChatGPT Infrastructure Applied AI Engineering - San Francisco Apply now(opens in a new window) **About the Team** ChatGPT is a rapidly evolving system: new capabilities ship continuously, product surfaces change quickly, and usage patterns shift week-to-week. Supporting that pace requires infrastructure that can handle real production constraints—high concurrency, unpredictable traffic patterns, complex dependency graphs, and frequent change. The ChatGPT Infrastructure team builds and operates the platforms that enable fast iteration without compromising performance or reliability. We design shared systems, data paths, rollout mechanisms, and reliability guardrails that teams rely on to ship changes to ChatGPT at scale. We focus on high-leverage infrastructure: primitives and “golden paths” that incorporate operational lessons as defaults, so engineers don’t need to rediscover failure modes, latency pitfalls, or integration issues each time they build something new. **About the Role** We’re hiring Senior and Staff Engineers to design and build infrastructure systems that underlie ChatGPT and multiply the effectiveness of teams building user experiences. This is not a support-only role. It’s a platform-building role: you’ll define interfaces, develop core abstractions, and create tooling to make safe, fast iteration the norm. Your work will reduce friction, prevent regressions, improve performance, and ensure systems scale gracefully as the product grows. **Where You Can Have Impact** * You might work on one or more of the following areas (without being restricted to any single area): + **Platform foundations & frameworks:** Core libraries, service frameworks, and shared components that standardize system building, integration, and evolution. + **Scalability & performance primitives:** Patterns and infrastructure that reduce tail latency, improve throughput, and keep costs predictable as demand increases. + **Reliability guardrails:** Mechanisms that prevent outages by design—rate limiting, load shedding, dependency isolation, backpressure, safe fallbacks, and robust regression controls. + **Developer productivity via golden paths:** Paved roads for common workflows (data access patterns, service integration, request lifecycle) that are fast, safe, and easy to use. + **Observability & debugging systems:** Instrumentation, metrics models, and investigative tooling that turn vague symptoms (“it’s slow”) into precise, actionable diagnoses. + **Safe change management:** Deployment and rollout systems that support rapid iteration with confidence—progressive delivery, automated verification, and fast rollback strategies. + **Interface and contract design across boundaries:** Clean APIs and stable contracts that reduce coupling and enable independent evolution across a complex ecosystem. **What You’ll Do** + Build and evolve infrastructure platforms used by many engineers and services. + Translate real-world constraints into clean abstractions: simple APIs, enforceable contracts, safe defaults. + Drive improvements in reliability and performance through principled design, measurement, and iterative hardening. + Partner with engineering and product teams to identify systemic pain points and develop reusable solutions. + Own outcomes end-to-end: design → implementation → rollout → operational maturity. **Qualifications** *Minimum Qualifications* + Experience building and operating large-scale distributed systems in production (high throughput, concurrency, and failure handling). + Strong fundamentals in systems design, including caching, consistency, queueing/backpressure, and resilient dependency management. + Ability to reason about performance (latency distributions, tail behavior, bottlenecks) and translate analysis into concrete engineering work.