About the Role
We're hiring an Early Access Deployment Engineer to lead technical engagements with customers who are leveraging our frontier capabilities to solve real-world use cases. You will work at the earliest-and often messiest-stage of development, when capabilities are still unclear, tooling and processes are evolving, and the path from promising technology to a valuable real-world application has yet to be defined.

You will be a hands-on builder, problem solver, and technical partner to customers and our research/product team. You'll push beyond initial assumptions, identify high-value use cases, prototype solutions, design useful evaluations, and troubleshoot what is and is not working.

Managing multiple customer engagements at once, you will help customers navigate ambiguity and difficult technical decisions while translating their experience into clear, actionable feedback for Research and Product.

You will also own the end-to-end execution of early access programs-from onboarding customers, supporting live experimentation, synthesizing findings, and informing launch decisions. You'll collaborate deeply with Research, Product, Engineering, Applied Evals, GTM, Legal, Security, Marketing, and other launch partners to create clarity, manage risk, and keep programs moving through changing conditions.

Success in this role means turning frontier capabilities into real-world customer value and high-quality research signals. You will develop reusable technical approaches from early deployments, improve how OpenAI learns from customers, and provide strong PoV, based on customer feedback, on what should be built, refined, or scaled.

This role is based in San Francisco. We use a hybrid work model of three days in the office per week and offer relocation assistance to new employees.

In this role, you will:

• Engage deeply with strategic customers during the earliest alpha stages to understand their goals, workflows, technical constraints, and highest-value opportunities.
• Translate emerging and ambiguous model capabilities into scoped use cases, working prototypes, and clear success criteria.
• Prototype rapidly with customers, Product, and Research through experiment-driven iteration.
• Design and support evaluations that reveal model behavior, solution quality, workflow impact, and failure modes.
• Evaluate and troubleshoot based feedback across product, model behavior to determine patterns and gaps
• Own early access program execution end to end, from customer onboarding and live experimentation through synthesis and launch decisions.
• Align Research, Product, Engineering, GTM, Legal, Security, Marketing, and launch teams by communicating risks, tradeoffs, and recommendations clearly.
• Codify architectures, evaluation methods, technical patterns, and program learnings into reusable playbooks that improve future deployments.

You might thrive in this role if you:

• Have 4+ years of software engineering or equivalent technical experience, including meaningful customer-facing delivery and ownership of production systems.
• Have a track record of rapidly turning ambiguous ideas into working prototypes and carrying the strongest approaches through to reliable solutions. Can demonstrate examples of systems thinking.
• Be proficient in Python and/or JavaScript or TypeScript, comfortable across modern front-end and back-end development, and familiar with cloud deployment.
• Bring practical AI and LLM depth, including experience reasoning about model behavior, designing evaluations, and building production-oriented AI applications.
• Exercise strong product and customer judgment by identifying the highest-value problem, scoping the right experiment, separating signal from noise, and balancing speed with technical integrity.
• Own problems end to end, learn unfamiliar technical or domain concepts quickly, and impose useful structure without waiting for perfect information.
• Manage several high-stakes technical engagements at once while anticipating blockers, adapting plans, and maintaining momentum across customers and internal teams.
• Communicate complex ideas with clarity and empathy, challenge assumptions constructively, and build trust with technical and non-technical stakeholders.