Partner-Facing Applied AI Engineer for Plugins

OpenAI; San Francisco (CA); Hybrid; USD 150,000 - 200,000; Full time

Job summary

OpenAI is seeking an Applied AI Engineer on the Ecosystem Applied AI Engineering team in San Francisco. You will design, build, evaluate, submit, and maintain high-utility plugins for ChatGPT and Codex, working with partner teams across product, engineering, legal, policy, and GTM.

This hands-on, partner-facing role requires turning ambiguous ideas into production-ready integrations, writing code and guidance, and driving smooth launches while ensuring reliability, privacy, and user value.

Qualifications

• 4–6 years of professional software engineering experience.
• Experience building production APIs, backend services, developer platforms, apps, plugins, or integrations.
• Strong product sense and ability to translate partner needs into actionable guidance.
• Excellent written and verbal communication for partner-facing work.

Responsibilities

• Own the technical partner journey from pitch to maintenance.
• Identify strong plugin use cases and define user journeys with measurable value.
• Write production code, prototypes, and reference implementations.
• Debug API contracts, OAuth/login, tool invocation, latency, retries, and observability across systems.
• Review partner architectures for API design, data handling, safety, and long-term maintainability.
• Contribute fixes and improvements to ChatGPT, Codex, and the plugins platform.
• Collaborate with product, engineering, design, partnerships, legal, policy, and GTM teams.

Skills

4-6 years software eng

API design

OAuth

distributed systems

partner-facing

Tools

APIs

SDKs

Webhooks

Auth flows

Job description

OpenAI is seeking an Applied AI Engineer on the Ecosystem Applied AI Engineering team in San Francisco. You will design, build, evaluate, submit, and maintain high-utility plugins for ChatGPT and Codex, working with partner teams across product, engineering, legal, policy, and GTM.

This hands-on, partner-facing role requires turning ambiguous ideas into production-ready integrations, writing code and guidance, and driving smooth launches while ensuring reliability, privacy, and user value.