AI​/ML Engineer - Post-Deployment

Why Mayo Clinic

Mayo Clinic is top-ranked in more specialties than any other care provider according to U.S. News & World Report. As we work together to put the needs of the patient first, we are also dedicated to our employees, investing in competitive compensation and comprehensive benefit plans– to take care of you and your family, now and in the future. And with continuing education and advancement opportunities at every turn, you can build a long, successful career with Mayo Clinic.

• Medical:
  
  Multiple plan options.
• Dental:
  
  Delta Dental or reimbursement account for flexible coverage.
• Vision:
  
  Affordable plan with national network.
• Pre-Tax Savings:
  
  HSA and FSAs for eligible expenses.
• Retirement:
  Competitive retirement package to secure your future.

AI/ML Engineers in AI Validation & Monitoring (AVM) apply data, systems, computer science, clinical workflow, and governance expertise to help ensure that post-deployment monitoring, reporting, and lifecycle evidence for clinical AI products is traceable, decision-ready, and supported by scalable governance technology. They work with AIA Governance Operations, AIA Governance Technologies, clinical and product teams, data and analytics partners, IT, architecture, patient safety, legal and regulatory functions, vendors, and other stakeholders to translate approved AIA Governance Policy, PDM and PDRS requirements, and evidence expectations into practical workflows, specifications, and review outcomes.

As the AI/ML Engineer - Post-Deployment Governance, with the functional assignment of PDRS TRex and Governance Technology Partnership, you will convert approved PDM and PDRS, evidence-lineage, reviewer, and policy requirements into functional requirements for TRex, dashboards, and related governance technology. You will support subject-matter review of PDM and PDRS assessment content when tooling, telemetry, data availability, evidence lineage, reporting workflow, or technology constraints affect governance adequacy;

define workflows, evidence objects, required fields, decision states, business rules, traceability, and governance-acceptance criteria; and partner with AIA Governance Technologies on feasibility, backlog refinement, prototypes, user acceptance, release readiness, and defect impact.

• Eliciting and prioritizing requirements from approved policy, PDM and PDRS assessment reviews, recurring evidence and workflow gaps, Product Lead feedback, and post-deployment governance priorities; maintaining a traceable requirements backlog and prioritized roadmap recommendations.
• Translating governance policy and evidence needs into user stories, workflow specifications, data definitions, required fields, decision states, business rules, acceptance scenarios, and functional test cases.
• Defining reusable TRex content models and evidence objects that preserve policy-to-workflow and requirement traceability across metrics, sources, owners, cadence, product and model versions, limitations, actions, and handoffs.
• Developing dashboard and portfolio-reporting requirements that support PDRS status, evidence confidence, conditions, escalation, change and retesting, ownership, next actions, and decision-ready visibility.
• Reviewing PDM and PDRS content and supporting evidence prepared by AIA Governance Operations when tooling, telemetry, data availability, evidence lineage, or reporting workflow is material; documenting required corrections, limitations, and technology or evidence changes.
• Partnering with AIA Governance Technologies on technical feasibility, backlog refinement, prototypes, acceptance criteria, user-acceptance testing, release readiness, and assessment of defects or proposed changes against approved governance requirements.
• Pe…