Technical Deployment Lead, Forward Deployed Engineering (FDE) - SF

Openai; Deutschland; Vor Ort; EUR 70.000 - 100.000; Vollzeit

Benefits dieser Stelle

Relocation assistance

Hybrid work model

Zusammenfassung

OpenAI is seeking a Technical Deployment Lead to define and execute how complex systems are delivered to customers. You will partner with teams to ensure successful deployment, track progress, and manage dependencies in a hybrid working environment.

This role requires deep technical project management skills, customer engagement expertise, and the ability to drive change effectively. Additionally, OpenAI offers relocation assistance and a dynamic working model with significant interaction with customer teams.

Qualifikationen

• Deep technical project management expertise required.
• Experience in engaging with customers for solution delivery.
• Ability to drive change management for adoption.

Aufgaben

• Own the technical delivery plan for multiple interdependent work streams.
• Run day-to-day engineering execution across teams.
• Embed with customer teams for production deployments.
• Partner with Product and Research to support deployment goals.

Kenntnisse

Technical project management

Customer engagement

Prototyping

Change management

Jobbeschreibung

About the team

OpenAI’s Forward Deployed Engineering (FDE) team turns research breakthroughs into production-grade systems. We embed deeply with customers to solve high-leverage problems and act as the delivery engine for our most complex large-scale engagements. We move quickly from prototype to production and surface reusable patterns that shape our platform. We operate at the intersection of deployment and development – working closely with OpenAI Research, Product and Partnerships.

About the 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.

You will 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 to 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 San Francisco. We use a hybrid work model of 3 days in the office per week. We offer relocation assistance. Travel between 25%- 50% is required.

In this role, you will:

• Own the technical delivery plan for multiple interdependent work streams. 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 work streams land in time to support deployment goals.
• Codify solution patterns and evals. Extract reusable patterns and package field sign