Forward Deployed Engineer (FDE), Healthcare - NYC

The posting, as OpenAI wrote it

About the team

OpenAI’s Forward Deployed Engineering team partners with healthcare organizations to deploy production AI systems across clinical, operational, and member-facing workflows. We work at the boundary of customer deployment and core platform development, using customer engagements to define repeatable architectures, evaluations, integrations, and operating standards for complex, regulated healthcare environments.

About the role

We are hiring a Forward Deployed Engineer (FDE) to own end-to-end deployments of our models within healthcare organizations, including payers, providers, health systems, and healthcare technology companies. You will lead technical discovery, architecture, implementation, evaluation, productionization, and handoff, translating complex customer workflows, data, infrastructure, and regulatory constraints into production AI systems.

You will measure success through production adoption, measurable workflow impact, and evaluation loops that establish customer-specific benchmarks, acceptance criteria, and launch readiness. You’ll collaborate directly with customer technical and operational teams, alongside internal Business, Research, Product, Engineering, and Security partners, to deliver solutions and translate deployment learnings into product improvements. This role owns the technical solution; ownership of the commercial or executive relationship is not required.

This role is based New York City. We use a hybrid work model of 3 days in the office per week. We offer relocation assistance. Travel up to 50% is required.

In this role you will

• Own the technical solution end to end, from customer discovery and workflow scoping through architecture, hands-on implementation, evaluation, production deployment, adoption, and handoff.
• Partner credibly with customer engineers, operators, and domain experts to frame ambiguous problems, define scope, and translate payer, provider, or health-system workflows into technical requirements and measurable outcomes.
• Design and implement production AI applications and agentic systems that integrate with customer infrastructure, enterprise APIs, data platforms, electronic health records, claims systems, and operational tools.
• Build with appropriate safeguards for protected health information (PHI), HIPAA, privacy, security, authorization, governance, auditability, and other regulated-delivery requirements.
• Define and operationalize evaluations, validation evidence, human-review workflows, escalation paths, and launch criteria that measure model and system quality against customer-specific acceptance thresholds.
• Use evaluation results, error analysis, observability, and customer feedback to improve system reliability, performance, model selection, workflow impact, and production readiness.
• Distill deployment learnings into reference architectures, interoperability and integration patterns, evaluation harnesses, security controls, and reusable techni

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