Applied AI Engineer, Codex

As 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 OpenAI 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 Madrid office. We use a hybrid work model of 3 days in the office per week and offer relocation assistance to new employees.

• 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.

• 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.

• Influence customer engineering strategy by helping technical leaders understand how AI coding systems can reshape their software development lifecycle, engineering practices, and organizational workflows.

• Have a demonstrated track record of designing, building, and delivering software or AI systems in enterprise environments, including taking systems from prototype to production. Relevant backgrounds may include applied AI or ML engineering, forward-deployed engineering, software engineering, developer tooling, customer engineering, solutions architecture, or technical consulting.

• Can point to substantial personal contributions in code, architecture, evaluation, debugging, integrations, or production engineering-not only program, enablement, or stakeholder management.

• Are highly proficient in Python and comfortable working across modern software development environments; experience with JavaScript, TypeScript, or another relevant language is valuable.

• Are an active user of AI coding tools and have developed a strong point of view on how AI can improve developer productivity and software engineering workflows.

• Enjoy building high-signal prototypes, integrations, automations, and production solutions that demonstrate and deliver what AI coding systems can enable.

• Understand how to evaluate AI coding systems systematically, including designing representative tasks, automated evaluations, production signals, and mechanisms for incorporating developer feedback.

• Have navigated enterprise production requirements such as developer tooling integrations, reliability, observability, security, privacy, data governance, performance, and cost.

• Can connect technical decisions to developer workflows, adoption, engineering productivity, and measurable business outcomes.

• Communicate with clarity and credibility across hands-on engineers, engineering leaders, security teams, product leaders, and executives.

• Are comfortable leading technical workshops and hands-on sessions that help engineering organizations adopt new development workflows and technologies.

• Bring high agency, strong technical judgment, and end-to-end ownership in ambiguous and rapidly evolving environments.

• Learn quickly, challenge assumptions constructively, and collaborate with humility; deep prior experience with OpenAI products is not required.