Applied AI Engineer, Codex | Tokyo
Tokyo, Japan • FullTime
Posted 7d ago
Remote Work Policy
On-site
Employment Type
FullTime
Categories
Applied AI Engineer
About the job
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.
Responsibilities
- Partner with engineering leaders and developers to identify high-value opportunities for Codex and translate them into technical architectures, implementation plans, evaluation strategies, and success criteria.
- Design, build, and deploy AI-powered software development workflows to improve planning, writing, testing, reviewing, debugging, and delivery of software.
- Build prototypes, evaluation harnesses, reference implementations, integrations, workflow automations, and production accelerators, often using Codex in the development process.
- Help customers transition from experiments to reliable production workflows, sustained developer adoption, and scaled impact.
- Design systematic approaches for evaluating AI coding systems using software engineering tasks, automated graders, production signals, and developer feedback.
- Make sound technical decisions regarding models, agents, tools, developer environments, integrations, reliability, observability, latency, cost, safety, security, and operational readiness.
- Diagnose complex implementation challenges, reproduce failures, test hypotheses, and resolve technical blockers.
- Lead technical deep dives, workshops, and enablement sessions to help engineering teams understand and adopt advanced AI coding workflows.
- Gather insights from real-world Codex deployments and translate them into product proposals, model feedback, and technical requirements.
- Create reusable architectures, tooling, examples, guides, and technical patterns to accelerate future Codex deployments.
- Influence customer engineering strategy by educating technical leaders on how AI coding systems can reshape their software development lifecycle and organizational workflows.
Requirements
- Demonstrated track record of designing, building, and delivering software or AI systems in enterprise environments, from prototype to production.
- Substantial personal contributions in code, architecture, evaluation, debugging, integrations, or production engineering.
- High proficiency in Python and comfort with modern software development environments.
- Experience with JavaScript, TypeScript, or another relevant language is valuable.
- Active user of AI coding tools with a strong point of view on AI's impact on developer productivity and software engineering workflows.
- Experience building high-signal prototypes, integrations, automations, and production solutions with AI coding systems.
- Understanding of systematic evaluation of AI coding systems, including task design, automated evaluations, production signals, and developer feedback mechanisms.
- Experience navigating enterprise production requirements such as developer tooling integrations, reliability, observability, security, privacy, data governance, performance, and cost.
- Ability to connect technical decisions to developer workflows, adoption, engineering productivity, and measurable business outcomes.
Benefits
- Relocation assistance to new employees.