Forward Deployed Engineer (FDE), Healthcare - SF

OpenAI•San Francisco, CA

2d•$198,000 - $294,000•Hybrid

About The Position

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. 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 in San Francisco. We use a hybrid work model of 3 days in the office per week. We offer relocation assistance. Travel up to 50% is required.

Requirements

6+ years of software engineering, ML/AI engineering, solutions engineering, technical consulting, or comparable experience, with the technical depth
Operated as a senior engineer, tech lead, or deployment owner who is trusted to make technical decisions in ambiguous environments.
Deeply hands-on and have personally owned technical discovery, architecture, implementation, evaluation, productionization, and handoff for complex customer-facing or enterprise systems.
Healthcare experience including payer workflows, provider operations, EHR systems, or interoperability standards such as Epic, HL7, and FHIR.
Exposure to provider or health-system workflows such as clinical operations, revenue cycle management, patient access, or contact centers; or to EHR and interoperability technologies such as Epic, Oracle Health/Cerner, MEDITECH, HL7, FHIR, or health information exchanges.
Shipped complex systems as a forward deployed or customer engineer, an engineer inside a payer, provider, or health system, a builder at an EHR, interoperability, revenue cycle, payer-tech, or healthcare infrastructure company, a hands-on technical consultant or integrator, a solutions architect for regulated enterprises, or a technical founder or early engineering leader.
Apply strong judgment to AI evaluation, privacy, security, governance, and reliability.

Nice To Haves

Prior FDE titles, clinical credentials, and experience across every healthcare domain are not required.

Responsibilities

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 technical primitives for healthcare and other regulated enterprise environments.