Forward Deployed Engineer (fde), Healthcare - Sf

OpenAI OpenAI · AI Frontier · San Francisco, CA · Forward Deployed Engineering

Forward Deployed Engineer (FDE) responsible for end-to-end deployment of AI models within healthcare organizations. This involves technical discovery, architecture, implementation, evaluation, productionization, and handoff, while considering complex customer workflows, data, infrastructure, and regulatory constraints (HIPAA, PHI). The role focuses on production adoption, measurable workflow impact, and establishing customer-specific benchmarks and acceptance criteria. It requires collaboration with customer teams and internal partners, and distilling learnings into reusable architectures and primitives for regulated enterprise environments.

What you'd actually do

  1. Own the technical solution end to end, from customer discovery and workflow scoping through architecture, hands-on implementation, evaluation, production deployment, adoption, and handoff.
  2. 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.
  3. 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.
  4. Build with appropriate safeguards for protected health information (PHI), HIPAA, privacy, security, authorization, governance, auditability, and other regulated-delivery requirements.
  5. 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.

Skills

Required

  • 6+ years of software engineering, ML/AI engineering, solutions engineering, technical consulting, or comparable experience
  • Senior engineer, tech lead, or deployment owner experience
  • Hands-on experience owning technical discovery, architecture, implementation, evaluation, productionization, and handoff for complex customer-facing or enterprise systems
  • Healthcare experience (payer workflows, provider operations, EHR systems, or interoperability standards like Epic, HL7, FHIR)
  • Experience with provider or health-system workflows (clinical operations, revenue cycle management, patient access, contact centers) or EHR/interoperability technologies
  • Experience shipping complex systems in roles like forward deployed engineer, customer engineer, solutions architect for regulated enterprises, or technical founder/leader
  • Strong judgment in AI evaluation, privacy, security, governance, and reliability

Nice to have

  • Clinical credentials
  • Experience across every healthcare domain

What the JD emphasized

  • production AI systems
  • complex, regulated healthcare environments
  • end-to-end deployments
  • production AI systems
  • customer workflows, data, infrastructure, and regulatory constraints
  • production adoption
  • measurable workflow impact
  • evaluation loops
  • customer-specific benchmarks
  • acceptance criteria
  • launch readiness
  • technical discovery
  • architecture
  • implementation
  • evaluation
  • productionization
  • handoff
  • complex customer-facing or enterprise systems
  • healthcare experience
  • payer workflows
  • provider operations
  • EHR systems
  • interoperability standards
  • Epic
  • HL7
  • FHIR
  • provider or health-system workflows
  • clinical operations
  • revenue cycle management
  • patient access
  • contact centers
  • EHR and interoperability technologies
  • Epic
  • Oracle Health/Cerner
  • MEDITECH
  • HL7
  • FHIR
  • health information exchanges
  • forward deployed or customer engineer
  • engineer inside a payer, provider, or health system
  • builder at an EHR, interoperability, revenue cycle, payer-tech, or healthcare infrastructure company
  • hands-on technical consultant or integrator
  • solutions architect for regulated enterprises
  • technical founder or early engineering leader
  • AI evaluation
  • privacy
  • security
  • governance
  • reliability
  • HIPAA
  • PHI

Other signals

  • Deploying production AI systems
  • End-to-end deployments
  • Integrating with customer infrastructure
  • Production adoption and workflow impact
  • Customer-specific benchmarks and acceptance criteria