The opportunity
OpenAI’s Applied AI Engineering team helps organizations turn frontier AI capabilities into safe, reliable, and high-impact production systems. For software engineering organizations, this means helping customers adopt Codex and other OpenAI capabilities across the software…
What you'll do
Partner directly with engineering leaders and hands-on developers to identify: high-value opportunities for Codex and translate them into technical architectures, implementation plans, evaluation strategies, and measurable success criteria.
Design, build, and deploy AI-powered software development workflows that: improve how engineering teams plan, write, test, review, debug, and deliver software.
Work hands-on in code to build prototypes, evaluation harnesses, reference: implementations, integrations, workflow automations, and production accelerators—often using Codex as part of your own development process.
Help customers progress from promising experiments to reliable production: workflows, sustained developer adoption, and scaled impact across engineering organizations.
Design systematic approaches for evaluating AI coding systems using: representative software engineering tasks, automated graders, production signals, and developer feedback.
Make sound technical decisions across models, agents, tools, developer: environments, integrations, reliability, observability, latency, cost, safety, security, and operational readiness.
What they're looking for
- Diagnose complex implementation challenges, reproduce failures, test: hypotheses, and drive technical blockers toward resolution.
- Lead technical deep dives, workshops, and hands-on enablement that help: engineering teams understand and adopt advanced AI coding workflows effectively and safely.
- Gather high-fidelity insights from real-world Codex deployments and translate: them into clear product proposals, model feedback, and technical requirements for OpenAI Product, Research, and Engineering teams.
- Create reusable architectures, tooling, examples, guides, and technical: patterns—including contributions to resources such as the OpenAI Cookbook—that accelerate future Codex deployments.
