About the job
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.
Qualifications
Minimum
- Deeply experienced, hands-on product engineer—typically with 10+ years building production software—who combines exceptional technical depth with strong product judgment.
- Ability to independently architect and implement sophisticated systems across frontend, backend, APIs, data, distributed services, and complex enterprise integrations.
- Ability to earn trust with customers, influence senior stakeholders, and bring cross-functional teams to a clear decision without relying on formal authority.
- Knowledge of how to turn uncertainty into a disciplined execution plan, using experiments, evaluations, instrumentation, and customer evidence to decide what to build.
- Treat enterprise identity, permissions, privacy, security, performance, reliability, and operational readiness as foundational product requirements.
- 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.
- 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
- 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.