Frontier Deployment Engineer — Early Access

OpenAI; San Francisco (CA); Hybrid; USD 250,000 - 330,000; Full time

Benefits offered by this job

Relocation assistance

Hybrid work model

Job summary

OpenAI is seeking an Early Access Deployment Engineer to lead technical engagements with customers exploring frontier capabilities, solving real‑world use cases, and shaping early deployment playbooks.

Based in San Francisco with a hybrid work model, you will own end‑to‑end program execution, prototype with customers, evaluate model behavior, and translate findings into actionable feedback for Research and Product teams.

Qualifications

• 4+ years of software engineering or equivalent technical experience.
• Proficient in Python and/or JavaScript/TypeScript, comfortable across front‑end and back‑end development, and familiar with cloud deployment.
• Practical AI/LLM depth, including evaluating model behavior and building production‑oriented AI applications.

Responsibilities

• Engage deeply with strategic customers during early alpha stages to understand goals and high‑value opportunities.
• Translate emerging capabilities into scoped use cases and prototypes with clear success criteria.
• Prototype rapidly with customers, Product, and Research through experiment‑driven iteration.
• Design and support evaluations that reveal model behavior and solution quality.
• Own end‑to‑end execution of early access programs from onboarding to launch decisions.
• Align cross‑functional teams by communicating risks, tradeoffs, and recommendations clearly.
• Codify architectures and learnings into reusable playbooks for future deployments.

Skills

Software engineering

Python

JavaScript/TypeScript

Cloud deployment

Customer-facing

Job description

OpenAI is seeking an Early Access Deployment Engineer to lead technical engagements with customers exploring frontier capabilities, solving real‑world use cases, and shaping early deployment playbooks.

Based in San Francisco with a hybrid work model, you will own end‑to‑end program execution, prototype with customers, evaluate model behavior, and translate findings into actionable feedback for Research and Product teams.