OpenAI · Engineering · Unspecified · Posted 2026-07-29
Software Engineer, Enterprise Verticals
OpenAI · San Francisco · $293k–325k base
This range's midpoint is above 86% of posted engineering ranges at AI companies right now. See the salary index.
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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 a full-stack product engineer who can own ambiguous enterprise workflows end to end: understand a customer problem, shape the product, build across frontend and backend, work through platform dependencies, instrument quality, and learn quickly with design partners.
You will build across ChatGPT Work surfaces, services, plugins, connectors, and data or permission boundaries when the experience requires it. You will make quality and rollout observable through evaluations, product and operational signals, and clear fallback or rollback paths.
This is a product-engineering role for someone who can move between user problems and system details without losing ownership of either. The strongest candidates will be able to turn specific customer evidence into a generalizable product, explain scope and architecture tradeoffs, and carry a feature from an early prototype through a bounded production rollout.
IN THIS ROLE, YOU WILL:
- Build and ship role-specific workflows across ChatGPT Work surfaces, services, plugins, and connectors.
- Turn customer and design-partner evidence into clear product decisions, milestones, and measurable quality gates without overfitting to a single customer.
- Partner with Design, Research, GTM, Security, and platform teams.
- Create robust evaluations, observability, and rollback paths.
- Own the full loop from prototype through bounded rollout and iteration.
- Define explicit contracts and fallback paths with shared platform teams when a workflow depends on connectors, permissions, model routing, or other cross-team systems.
YOU MIGHT THRIVE IN THIS ROLE IF:
- You are a strong product engineer with excellent product judgment.
- You are comfortable moving across frontend, backend, APIs, data, and systems integration.
- You communicate clearly with customers and cross-functional partners.
- You use evidence and tests to make ambiguous work observable.
- You care about enterprise permissions, reliability, privacy, and rollout safety.
- You have shipped a user-facing product through ambiguity and can explain the decisions, tradeoffs, and evidence behind it.
- You can translate a concrete customer workflow into a general product without losing the details that make the first experience useful.
PREFERRED QUALIFICATIONS:
- Experience building and operating full-stack production products across a modern frontend stack (such as React/TypeScript) and backend services (such as Python, Go, Node.js, or comparable technologies).
- Experience designing evolvable APIs, data models, and distributed or integration-heavy systems, including authentication, identity, permissions, secure data flows, and partial-failure behavior.
- Experience owning production systems at scale, including observability, performance, testing, incident response, safe migrations, staged rollout, and rollback.
- Experience building applied-AI or conversational products and evaluating probabilistic behavior with grounding, quality signals, and human-feedback loops.
- Experienc …
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