Lead Technology Product Manager, AI Quality & Evaluation

Build the product capabilities that help ensure AI-enabled healthcare experiences are useful, trusted, and responsibly deployed.

At Wolters Kluwer Health, the UpToDate suite supports millions of clinical decisions every day. As AI becomes part of healthcare workflows, quality cannot be treated as a one-time launch checkpoint. AI experiences need to be evaluated,monitored, and improved continuously so clinicians and care teams can trust the information and interactions they rely on.

As Senior Product Manager, AI Quality & Evaluation, you will play a core role in translating clinical quality standards and evaluationstrategyinto scalable product capabilities. Working under product leadership and in close partnership with clinical, technical, and data experts, you will help define the requirements, workflows, tooling, automation, and reporting needed to evaluate AI-enabled experiences before and after launch.

This role does not own the clinical quality standard or statisticalmethodology. It helps make those standards operational at scale through usable tools, repeatable workflows, measurable signals, andclear evidencethat supports product decisions, customer trust, and responsible AI.

What You'll Do

• Translate expert-defined quality standards and evaluation methods into product requirements, workflows, and roadmap priorities.

• Drive discovery and delivery for defined AI quality and evaluation capabilities, including tooling, automation, monitoring, evidence capture, and reporting.

• Partner with clinical, data science, engineering, product, legal, compliance, and customer-facing teams to operationalize AI evaluation before and after launch.

• Identifyopportunities to automate and scale evaluation workflows while preservingappropriate expertoversight.

• Define product requirements for review workflows,issuetriage, launch readiness, post-launch monitoring, and quality reporting.

• Use evaluation insights to inform product improvements, user experience, content quality, model behavior, and operational processes.

• Track emerging market practices and customer expectations related to AI quality, evaluation, benchmarking, and responsible AI.

• Translate AI quality and evaluation practices into clear narratives for product leaders, customer-facing teams, and internal stakeholders.

What You'll Bring

• 5+ years of product management experience in healthcare, enterprise SaaS, data-driven products, platform products, quality, or related areas.

• Strong product discovery and execution skills, includingrequirementsdefinition, roadmap planning, experimentation, and metrics-driven decision-making.

• Ability to translate complex clinical, technical, quality, or risk concepts into practical product requirements and workflows.

• Experience working with engineering, data, clinical, compliance, customer-facing, or operational stakeholders.

• Strong analytical thinking and comfort defining metrics, dashboards, workflows, and decision criteria.

• Strong communicationskills, especially around quality, trust, tradeoffs, evidence, and customer value.

• Curiosity and learning agility around AI, evaluation methods, responsible AI, and emerging technologies.

Preferred Background

• Experience with AI-enabled products, healthcare technology, quality tooling, data products, search/retrieval quality, content quality, clinical validation, or decision-support products.

• Familiarity with AI evaluation methods, benchmarks, monitoring, human-in-the-loop review, automation, or applied machine learning concepts.

• Experience in healthcare, clinical workflows, regulated environments, or professional-information products.

• Experience supporting customer-facing narratives around product quality, trust, or responsible technology.

What Success Looks Like

• Expert-defined quality standards are translated into scalable product workflows, tools, and requirements.

• Evaluation processes become more automated, repeatable, measurable, and usable by cross-functional teams.

• Product teams have clearer quality signals and launch-readiness criteria for AI-enabled experiences.

• Evaluation insights improve product quality, customer trust, user experience, and responsible AI practices.

• Customer-facing teams can clearly explain how AI quality and trust are evaluated.
  
  
  
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