Technical Deployment Lead, Forward Deployed Engineering - Tokyo

About the teamOpenAI’s Forward Deployed Engineering team partners with customers to turn research breakthroughs into production systems. We operate at the intersection of customer delivery and core platform development.About the RoleThis is a founding role. As a Technical Deployment Lead (TDL), you will define how OpenAI delivers complex systems to customers. You will own how they are built, shipped, and adopted. You’ll translate business outcomes into a technical plan, run day-to-day execution across FDEs, Researchers, and Customer Engineers, and partner with customer teams to ensure delivery supports their goals.This is not a management role, however you'll own delivery end-to-end: embedding with customers to map workflows and success criteria, ensuring components ship on time, and leading readiness and change management for adoption. You’ll track progress, manage dependencies, make sequencing decisions, and drive 0→1 prototypes through MVP and scale. You will also share field insights with Product and Research to guide roadmap and priorities.Success will be measured first and foremost by impact - deployments that deliver measurable value against customer goals, drive adoption, and become critical to their workflows. Additional measures of success include delivery reliability (milestones hit, low reopen/churn), operating leverage (patterns reused across deployments), judgment under pressure, and product impact (field signal that shifts roadmaps/architectures).This is a high-trust, high-autonomy role. Success requires deep technical project management expertise, extreme ownership of outcomes, and an ability to immerse in customer workflows and partner with customer teams to solve complex engineering problems at pace.This role is based in Tokyo. We use a hybrid model of 3 days in office and offer relocation assistance. Travel up to 25-50% is required.In this role, you will:Own the technical delivery plan for multiple interdependent workstreams. Translate business objectives into a roadmap with milestones, dependencies, and acceptance criteria.Run day-to-day engineering execution. Track and drive delivery across OpenAI FDE and customer teams. Keep progress unblocked and sequenced. Make real-time trade-offs on scope and priority to protect the critical path.Embed with customer teams to land production deployments and drive adoption. Map workflows, shape tools/integrations, and translate requirements into a delivery plan. Lead onboarding, adoption, and change management.Partner with Product and Research so platform components and research workstreams land in time to support deployment goals.Codify solution patterns and evals. Extract reusable patterns and package field signals to improve product and models.Own value cases and ROI. Set impact hypotheses, baselines, and KPIs; run pre-/post-deployment measurement and report to exec sponsors.You’ll thrive in this role if you:Bring 7+ years of customer‑facing technical delivery leadership.Track record of successfully leading large, complex, high-stakes customer engagements where customer outcomes depended on tight coordination and fast decision making, ideally involving AI.Excel in high ambiguity environments. Know how to simplify complex and dynamic work.Move fluidly between system level understanding and execution level detail; can dive into customer workflows/data, map constraints, sketch architectures and move ambiguous problems to shipped systems.Think strategically and pattern-match. Able to step back from execution detail, recognise broader trends across deployments, and connect customer needs to scalable, reusable solutions.Have strong technical fluency and sharp sequencing instincts. Confident discussing technical details, pressure-testing architectures, and making trade-offs.Have shipped AI/LLM systems. You understand solution patterns, integration basics, and production pitfalls.You’re a translator with executive presence. You make complex technical trade-offs legible to bu ... (truncated, view full listing at source)