Principal AI​/ML Engineer - Post Deployment Governance

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.

As the Principal AI/ML Engineer — Post-Deployment Governance within AI Validation & Monitoring (AVM), you will serve as the enterprise technical and methodological authority for post-deployment monitoring and reporting, measurement, lifecycle evidence, and Post Deployment Monitoring (PDM) and Post Deployment Reporting Summary (PDRS) governance. You will define risk-proportionate AIA Governance requirements and standards for monitoring readiness; performance and functionality; patient safety;

adoption and fidelity; outcomes; change and retesting; metrics, formulas, baselines, targets, and thresholds; subgroup interpretation; uncertainty; and evidence confidence. You will apply data science, AI/ML engineering, statistical, and systems expertise to determine whether evidence is traceable, appropriately interpreted, proportionate to risk, and decision-ready.

Within AIA Governance, you will review drafted monitoring, reporting, measurement, and PDRS content; direct corrections and alternate approaches; consult on complex cases; establish precedent; and elevate unresolved technical or policy issues.

• Provide strategic and technical leadership for enterprise post-deployment governance, measurement, monitoring and reporting, and PDM and PDRS standards.
• Define risk-proportionate requirements across pilot, full implementation, post-deployment change, recurring PDRS, and legacy-product pathways.
• Establish standards for signals, metrics, formulas, baselines, targets, thresholds, uncertainty, evidence confidence, outcomes, and subgroup interpretation.
• Define monitoring-readiness expectations for sources, owners, collection methods, cadence, versions, limitations, lineage, Data Cards, Model Cards, handoffs, and sustainable ownership.
• Provide authoritative SME review of Governance Operations Product Lead assessment content and evidence for policy alignment, sufficiency, traceability, methodological adequacy, and decision readiness.
• Apply data science, statistical, AI/ML engineering, and systems methods to assess metric validity, source fitness, threshold logic, analyses, limitations, and conclusions.
• Review observability, logging, telemetry, workflow signals, version context, change detection, and monitoring and reporting continuity through significant changes.
• Own complex or precedent-setting questions involving monitoring, thresholds, evidence insufficiency, vendor limitations, significant change, revalidation continuity, lifecycle action, PDRS, or CAIO escalation.
• Recommend corrections, alternate methods, interim controls, additional evidence, action plans, re-review, retesting, or revalidation.
• Set precedent, issue final AVM direction, and elevate policy, clinical, cross-domain, or enterprise impasses.
• Lead PDRS templates and rubrics, evidence-confidence and escalation methods, metric libraries, executive presentation standards, and governance acceptance criteria.
• Convert recurring gaps into policy, playbooks, standard findings, rubrics, examples, training, calibration, and Product Lead enablement.
• Define enterprise requirements for TRex workflows, evidence objects, traceability, dashboards, portfolio visibility, and reusable governance capabilities.
• Coordinate with product teams, vendors, platforms, legal, committees, and enterprise groups on methods, tooling, specifications, and ownership.
• Provide clear complex-case findings that communicate limitations, confidence, required actions, and escalation triggers to technical and non-technical audiences.
• Mentor and calibrate engineers, analysts, and Product Leads; foster consistent methods and cross-lane coordination with Validation & Evaluation.
• Support audit sampling, quality assurance, enterprise learning, and continuous…