Associate AI/ML Engineer- Post Deployment Governance

Associate AI/ML Engineers in AI Validation & Monitoring (AVM) support enterprise clinical AI governance by producing standardized, traceable quantitative evidence for post-deployment monitoring (PDM), post-deployment report summary (PDRS) review, recurring reporting, and portfolio visibility. Working under approved methods and the guidance of more senior AVM and quantitative leads, they collaborate with AIA Governance Operations, product and reporting owners, data teams, and other partners to verify metrics, data sources, formulas, units, baselines, targets, thresholds, cadence, subgroup information, limitations, and lineage.

As the Associate AI/ML Engineer - Post-Deployment Governance, serving in the functional assignment of Quantitative Analytics, PDRS, and Portfolio Support, you will perform calculations, metric and data-source mapping, baseline-to-target comparisons, trend analysis, subgroup summaries, and evidence-quality checks. You will support quantitative PDM/PDRS appendices, portfolio dashboards, recurring reporting, cross-product evidence analysis, and review of PDM/PDRS content prepared by the AIA Governance Operations. You will apply established formulas, metric definitions, thresholds, and interpretation methods, identify final adequacy, or provide final AVM concurrence recommendations.  

• Mapping outcomes and governance requirements to metrics and data sources and maintaining metric-to-source and outcome-to-metric crosswalks with ownership, cadence, version, limitation, and lineage information.
• Performing approved calculations, baseline-to-target comparisons, trend analyses, subgroup summaries, and evidence-quality checks using established formulas, definitions, thresholds, and interpretation methods.
• Verifying source, formula, unit, cadence, baseline, target, threshold, limitation, version, and lineage information for PDM/PDRS appendices, recurring reports, and portfolio analyses.
• Preparing standardized quantitative summaries, Metrics and Data Source Appendix content, portfolio dashboard views, and reporting-status summaries for AVM and Governance Operations review.
• Reviewing draft PDM/PDRS quantitative content for completeness, internal consistency, traceability, and correct application of approved methods; documenting draft corrections, clarification questions, and evidence gaps.
• Identifying recurring metric, data-quality, ownership, lineage, threshold, subgroup, and reporting gaps across the portfolio and organizing themes for policy, training, template, dashboard, and tool improvement.

• Escalating disputed thresholds, uncertain interpretation, material risk signals, anomalous findings, or the need for a new analytical method to the assigned Principal, Engineer, or Senior reviewer rather than independently resolving non-routine methodology. 

  
  

  This vacancy is not eligible for sponsorship/ we will not sponsor or transfer visas for this position. Also, Mayo Clinic DOES NOT participate in the F-1 STEM OPT extension program.
   

•  A bachelor’s degree in engineering, computer science, health science, or a related field
• Knowledge in applying AI and machine learning in production environments, showcasing an understanding of healthcare technology.
• Knowledge in cloud infrastructure environment and software development tools.
• Skill in AI/ML techniques and frameworks.
• Skill in collaborating across diverse teams and effectively communicating complex technical concepts to non-technical stakeholders.
• Familiarity with best practices in data engineering, data science, AI Engineering, and the MLOps communities.
• Strong interpersonal, communication, and time management skills.
   

Preferred Qualifications: 

• Knowledge of the healthcare domain, including clinical workflows, electronic health records, medical terminologies, regulatory requirements, and industry standards.
• Familiarity with systems or quality engineering best practices, regulatory standards, and compliance frameworks, with the ability to adapt these effectively to different project scenarios.
• Ability to articulate complex technical concepts to diverse audiences, facilitating clear understanding and engagement from technical and non-technical stakeholders.
• Ability to manage a varied workload of projects with multiple priorities and stay current on healthcare trends.
• Demonstrated hands-on experience using the TRex assessment application and evaluation and development of clinical AI outcome measures using metrics, formulas, baselines, targets, thresholds, source lineage, trend and subgroup analyses.