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OpenAI is seeking an Applied AI Engineer focused on Codex to partner with leading engineering teams to design, build, and deploy AI-powered software development workflows. You will work hands-on to implement prototypes, evaluation harnesses, and production accelerators, often using Codex in your own development process.
You will help customers make technical decisions around model behavior, integration environments, security, and reliability, translating deployment experience into product
About the TeamOpenAI’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 the company capabilities across the software development lifecycle—transforming how teams plan, build, test, review, and deliver software.
We work directly with engineering leaders and hands-on developers to identify high-value opportunities, design and implement AI-powered development workflows, and scale what works across engineering organizations. We turn lessons from these deployments into better products, reusable architectures, and technical patterns that help developers everywhere get more value from Codex.
About the RoleAs an Applied AI Engineer focused on Codex, you will partner directly with leading engineering organizations to design, build, and deploy AI systems that transform how software is developed. You will combine deep technical judgment, hands-on engineering, and customer leadership to take ambitious ideas from workflow and use-case selection through prototyping, evaluation, production rollout, and scaled adoption.
You will work alongside engineering teams to build advanced AI coding workflows, integrations, automations, and evaluation systems—often using Codex itself as part of your development process. You will help customers make technical decisions involving model behavior, agentic workflows, developer environments, security, reliability, evaluation, and operational readiness, while ensuring deployments translate into measurable improvements in engineering productivity and software delivery.
You will work closely with the company Product, Research, Engineering, Security, Sales, and the broader Codex organization, translating real-world deployment experience into high-signal product and model feedback. Success is measured by production systems, sustained developer adoption, and meaningful improvements to how engineering organizations build software—not simply successful demonstrations or enablement activity.
This role is based in our London office. We use a hybrid work model of 3 days in the office per week and offer relocation assistance to new employees.
In this role, you will:
You’ll thrive in this role if you:
About the company
the company is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of humanity.
We are an equal opportunity employer, and we do not discriminate on the basis of race, religion, color, national origin, sex, sexual orientation, age, veteran status, disability, genetic information, or other applicable legally protected characteristic.
For additional information, please see the company’s Affi …
Background checks for applicants will be administered in accordance with applicable law, and qualified applicants with arrest or conviction records will be considered for employment consistent with those laws, including the San Francisco Fair Chance Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act, for US-based candidates. For unincorporated Los Angeles County workers: we reasonably believe that criminal history may have a direct, advers