Applied AI Engineer, Enterprise - EDU
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—then turn those lessons into better OpenAI products and reusable deployment patterns. ## About the Role As an **Enterprise AIE** (Applied AI Engineer) you’ll serve as a technical partner to leading organizations—including **universities and educational institutions**—helping them 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. You’ll write and debug code, build evaluation systems, resolve complex integrations, and guide decisions across **model behavior, reliability, latency, cost, safety, security, governance, and operational readiness**. **Success looks like** durable production systems, sustained adoption, and meaningful business impact. Your deployment experience will also directly influence how OpenAI’s products evolve. ## Location / Work Model - Based in **New York City** or **San Francisco**, or **remote** - Office-based employees follow a **hybrid model**: **3 days/week in the office** - **Relocation assistance** available for new employees ## In This Role, You Will - **Hands-on**: 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/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 - Collaborate closely with customers and OpenAI teams (Product, Research, Engineering, Security, Go-to-Market) to turn deployment experience into actionable product feedback - Create reusable architectures, tooling, playbooks, and technical guidance to accelerate future enterprise deployments ## You Might Thrive If You - Have personally designed, built, and shipped AI/ML systems in enterprise environments (prototype → production), 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 & data pipelines, evaluation, observability, integrations, and production operations (JavaScript/TypeScript or another relevant language is a plus) - Know how to evaluate AI systems systematically using representative data, automated graders, production signals, and human judgment - Have navigated enterprise production requirements: integrations, reliability, observability, security, privacy, data governance, performance, and cost - Have experience in education or research environments (institutional data, privacy, access controls, complex integrations) — preferred but not required - Can connect technical decisions to customer workflows, adoption, a
Listing freshness
CronJobs last confirmed this listing 44m ago. If its source stops confirming the opening for seven days, this page is removed from active inventory.