Job DescriptionAssociate AI/ML Engineers in AI Validation & Monitoring (AVM) support the enterprise clinical AI governance review and consultation function for post-deployment monitoring, reporting, measurement, and lifecycle evidence. Working under the guidance of more senior team members, they collaborate with AIA Governance Operations, product and operational teams, clinicians, data and platform partners, vendors, Governance Technology, and other stakeholders to organize evidence, apply approved methods, identify completeness and traceability gaps, and prepare decision-ready review materials that support safe, effective, and accountable use of AI in clinical and operational settings.
As the Associate AI/ML Engineer - Post-Deployment Governance, serving in the functional assignment of Monitoring, Reporting, and Lifecycle Enablement, you will prepare product-level evidence maps connecting required signals to source systems, owners, cadence, product and model versions, limitations, Post-Deployment Monitoring (PDM) and Post-Deployment Report Summary (PDRS) domains, actions, and handoffs. You will support monitoring and reporting readiness across full implementation, recurring reporting, post-deployment changes, and legacy products; collect and structure evidence using approved templates, checklists, and readiness methods; and prepare draft AVM comments, review findings, consultation materials, and escalation packages.
- Preparing and maintaining product evidence and lineage maps that connect monitoring signals, metrics, source systems, owners, cadence, collection methods, product and model versions, limitations, PDM and PDRS domains, actions, and reporting handoffs.
- Supporting monitoring and reporting readiness reviews for full implementation, recurring PDRS, post-deployment changes, and legacy products by applying approved templates, checklists, rubrics, evidence requirements, and readiness criteria.
- Performing completeness and traceability checks for sources, owners, cadence, versions, limitations, baselines, targets, thresholds, conditions, actions, and transition information; identifying missing, inconsistent, or unsupported evidence for assigned reviewers.
- Reviewing PDM and PDRS content and drafting factual corrections, clarification questions, evidence-gap and limitation summaries, standard AVM comments, and monitoring-readiness findings.
- Organizing PDRS review packages, evidence references, owner and source matrices, action and condition trackers, follow-up records, ownership verification, lifecycle handoff materials, and transition plans.
- Supporting post-deployment change and legacy-product reviews by tracing version, metric, baseline, threshold, monitoring-continuity, reporting-cadence, evidence-gap, and interim-control impacts for Engineer or Principal review.
- Coordinating evidence and status information with Product Leads, product and operational owners, data and platform partners, vendors, and other stakeholders; escalating substantive interpretation, unresolved risk, or complex PDM and PDRS questions.
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.
Qualifications- 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.
- Experience in user-centered design, human factors engineering, usability testing methodologies, and evaluation across AI product development. Ability to conduct expert reviews using established usability practices and methods. Presents findings in easy-to-understand terms for the business or clinical practice.
- 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 to build evidence maps, verify post-deployment monitoring readiness, ownership, cadence, versions, limitations, and lifecycle handoffs for AI tools deployed in Epic, ANIMATE, and comparable clinical environments.
