Applied AI Engineer, Enterprise - Healthcare Life Sciences (HLS)
OpenAI · San Francisco
About this role
## About the Team 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 also turn lessons from deployments into better OpenAI products and reusable patterns that help customers move faster. ## About the Role As an Enterprise AIE, you’ll serve as a technical partner to leading organizations in the Healthcare and Life Sciences segment (HLS). You’ll help turn ambitious AI use cases into durable production systems and drive adoption at scale. You’ll combine deep technical judgment with hands-on engineering to provide technical leadership on complex customer deployments—writing and debugging code, building evaluation systems, resolving complex integrations, and guiding decisions across model behavior, reliability, latency, cost, safety, security, governance, and operational readiness. You’ll 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. **Location:** New York City or San Francisco (hybrid: 3 days/week in office) or remote. Relocation assistance may be available. ## 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. - 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 teams (Product, Research, Engineering, Security, Go-to-Market) to turn deployment experience into actionable product feedback. - Partner with customer security, privacy, clinical, and business teams to design responsible AI deployments, including data handling, access controls, evaluation, human oversight, and monitoring. - Create reusable architectures, tooling, playbooks, and technical guidance to accelerate future enterprise deployments. ## You Might Thrive in This Role If You - Have personally designed, built, and shipped AI/ML systems in enterprise environments—taking them from prototype to production and contributing through coding, architecture, evaluation, debugging, or production engineering. - Are highly proficient in **Python** and comfortable working end-to-end across model APIs, agent/tool development, retrieval and data pipelines, evaluation, observability, integrations, and production operations. Experience with **JavaScript/TypeScript** (or another relevant language) is valuable. - Know how to evaluate AI systems systematically using representative data, automated graders, production signals, and human judgment. - Have navi
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