Enterprise AI Engineer, Healthcare & Life Sciences (Remote)

OpenAI, Inc.; San Francisco (CA); Hybrid; USD 180,000 - 240,000; Full time

Benefits offered by this job

Relocation assistance

Job summary

OpenAI is seeking an Enterprise Applied AI Engineer (AIE) to act as a technical partner for leading organizations in Healthcare and Life Sciences. You will design, build, and deploy durable AI-powered production systems, guiding model behavior, reliability, latency, cost, safety, and governance.

You will work across clinical, operational, administrative, and research workflows, translating security and governance requirements into practical deployment decisions.

Qualifications

• Have designed, built, and shipped AI/ML systems in enterprise environments from prototype to production.
• Experience with evaluating models, data pipelines and production readiness.

Responsibilities

• Design, build, evaluate, and deploy AI systems from prototype to production at scale.
• Partner with enterprise customers to translate opportunities into architectures and plans.
• Make sound technical decisions across model selection, tooling, retrieval, data pipelines and governance.
• Diagnose complex implementation challenges and drive resolution.
• Collaborate with customers' engineering teams and product, research, security, and GTM teams.
• Design responsible AI deployments with data handling, access controls and monitoring.
• Create reusable architectures, tooling, and playbooks for future deployments.

Skills

Python

AI/ML engineering

Enterprise deployments

Security & governance

Job description

OpenAI is seeking an Enterprise Applied AI Engineer (AIE) to act as a technical partner for leading organizations in Healthcare and Life Sciences. You will design, build, and deploy durable AI-powered production systems, guiding model behavior, reliability, latency, cost, safety, and governance.

You will work across clinical, operational, administrative, and research workflows, translating security and governance requirements into practical deployment decisions.