Principal Software Engineer, Enterprise Technology Vertical

OpenAI · San Francisco · $441K – $500K · Engineering

Posted 2026-08-12

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ABOUT THE TEAM

Enterprise Verticals builds role-specific ChatGPT Work experiences for high-value enterprise workflows. We combine product engineering, plugins and skills, connectors, data, evaluations, and customer evidence to turn useful demos into reliable daily work.

This opening sits within the Technology vertical inside Enterprise Verticals. The group focuses on repeatable workflows for people at technology companies, beginning with functions such as data and analytics, sales, and design, and carries the shared platform needs—tool integration, permissions, quality measurement, and safe rollout—across those experiences.

We work closely with Design, Research, GTM, Security, and platform teams, as well as with customers and design partners. Success means that people can reach a trustworthy first result, understand what the system did, and keep using the workflow—not merely that a prototype exists.

ABOUT THE ROLE

We are looking for an exceptionally experienced, hands-on full-stack engineer to define and build the next generation of AI-powered enterprise workflows. You will take on the hardest and most ambiguous problems in the Technology vertical: translating real customer needs into product direction, designing the systems behind the experience, and personally writing and shipping production-quality code across the stack.

You will own the technical direction and end-to-end delivery of products spanning ChatGPT Work surfaces, backend services, plugins, connectors, enterprise data, permissions, and evaluations. You will make foundational architecture and product tradeoffs; establish patterns other engineers can build on; and hold these experiences to a high bar for reliability, security, observability, and customer value.

This is an individual-contributor role for an engineer who leads through technical judgment, direct execution, and influence—not people management. You should be equally comfortable working directly with customers, setting direction with senior cross-functional and platform partners, debugging complex production behavior, and staying close to the code. Success means taking a meaningful enterprise product from first principles through adoption at scale.

RESPONSIBILITIES:

- Set the technical direction for role-specific enterprise AI experiences, and personally design, build, and ship their most critical components across ChatGPT Work surfaces, services, plugins, and connectors.

- Turn ambiguous customer and design-partner needs into a clear, generalizable product and technical strategy, with explicit milestones, architectural decisions, and measurable quality and adoption goals.

- Lead complex initiatives across Design, Research, GTM, Security, and platform teams; align senior stakeholders; make consequential tradeoffs; and drive decisions through to implementation.

- Architect production-grade systems and establish the evaluation, instrumentation, security, reliability, staged-rollout, and rollback standards required to operate probabilistic AI experiences safely.

- Own the complete product and engineering lifecycle: problem definition, technical design, hands-on prototyping, production implementation, launch, customer feedback, and sustained iteration.

- Define durable technical contracts and fallback strategies across connectors, identity, permissions, enterprise data, model routing, and shared platform dependencies; raise the engineering bar through architecture reviews, mentorship, and reusable patterns.

YOU MIGHT THRIVE IN THIS ROLE IF:

- You are a deeply experienced, hands-on product engineer—typically with 10+ years building production software—who combines exceptional technical depth with strong product judgment.

- You can independently architect and implement sophisticated systems across frontend, backend, APIs, data, distributed services, and complex enterprise integrations.

- You can earn trust with customers, influence senior stakeholders, and bring cross-functional teams to a clear decision without relying on formal authority.

- You know how to turn uncertainty into a disciplined execution plan, using experiments, evaluations, instrumentation, and customer evidence to decide what to build.

- You treat enterprise identity, permissions, privacy, security, performance, reliability, and operational readiness as foundational product requirements.

- You have repeatedly led the architecture and hands-on delivery of important user-facing products from ambiguous beginnings through production use, and can explain the technical and product decisions that made them succeed.

- You know when to build quickly, when to invest in foundational systems, and how to turn a specific customer workflow into a durable product that serves many customers.

PREFERRED QUALIFICATIONS

- Extensive experience personally building and operating full-stack production products, including modern frontend technologies such as React and TypeScript and backend services in Python, Go, Node.js, or comparable languages.

- A track record of setting technical direction for complex, integration-heavy or distributed systems, with deep understanding of APIs, data models, enterprise identity, authorization, secure data flows, and partial-failure behavior.

- Demonstrated ownership of business-critical production systems at scale, including architecture, performance, observability, testing, incident response, migrations, staged delivery, and operational excellence.

- Experience shipping applied AI, agentic, or conversational products, with a practical understanding of model behavior, tool use, grounding, evaluations, human feedback, and the reliability challenges of probabilistic systems.

- Experience designing and building sophisticated workflow, data, analytics, visualization, or insight products that make complex systems genuinely useful to enterprise users.

- A strong record of leading through technical judgment, mentoring experienced engineers, shaping cross-team architecture, and connecting engineering decisions to measurable customer outcomes and sustained adoption.

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