About the role
ABOUT THE TEAM
Across industries, OpenAI’s Applied AI Engineering (AIE) team helps organizations turn frontier AI into safe, reliable production systems that deliver meaningful business outcomes. We work directly with customers’ engineering, product, security, and transformation teams to navigate complex architectures, data environments, governance requirements, and organization-wide change. We turn lessons from these deployments into better OpenAI products and reusable patterns that help customers move faster.
ABOUT THE ROLE
As an Enterprise AIE, you will serve as a technical partner to leading organizations within the Healthcare and Life Sciences segment (HLS), helping them turn ambitious AI use cases into durable production systems and adoption at scale. You will combine deep technical judgment with hands-on engineering to provide technical leadership on complex customer deployments. You will write and debug code, build evaluation systems, resolve complex integrations, and guide decisions about model behavior, reliability, latency, cost, safety, security, governance, and operational readiness. You will work across clinical, operational, administrative and research workflows, translating security, privacy and governance requirements into practical deployment decisions. Success is measured by durable production systems, sustained adoption, and meaningful business impact. Your deployment experience will also directly influence how OpenAI’s products evolve.
This role may be based in one of our offices in New York City or San Francisco, or it may be performed remotely. Office-based employees follow a hybrid work model with three days per week in the office. We offer relocation assistance to new employees.
IN THIS ROLE, YOU WILL
- Work hands-on in code to design, build, evaluate, and deploy AI systems from prototype to reliable production and scaled adoption that solve important customer problems and deliver measurable business outcomes.
- Partner with enterprise customers to identify high-value opportunities and translate them into technical architectures, implementation plans, evaluation strategies, and success criteria.
- Make sound technical decisions across model selection, agent and tool design, retrieval, context design, data pipelines, evaluation, reliability, observability, latency, cost, safety, security, and governance.
- Diagnose complex implementation challenges, reproduce failures, test hypotheses, and drive blockers to resolution.
- Partner closely with customers’ engineering teams and OpenAI’s Product, Research, Engineering, Security, and Go-to-Market teams to turn deployment experience into actionable product feedback.