Lead Technology Product Manager, AI Quality & Evaluation
AI in this role
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 evaluation strategy into 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 statistical methodology. It helps make those standards operational at scale through usable tools, repeatable workflows, measurable signals, and clear evidence that 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.
Identify opportunities to automate and scale evaluation workflows while preserving appropriate expert oversight.
Define product requirements for review workflows, issue triage, 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, including requirements definition, 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 communication skills, 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.
#LI-Hybrid
Our Interview Practices
To maintain a fair and genuine hiring process, we kindly ask that all candidates participate in interviews without the assistance of AI tools or external prompts. Our interview process is designed to assess your individual skills, experiences, and communication style. We value authenticity and want to ensure we’re getting to know you—not a digital assistant. To help maintain this integrity, we ask to remove virtual backgrounds and include in-person interviews in our hiring process. Please note that use of AI-generated responses or third-party support during interviews will be grounds for disqualification from the recruitment process.
Applicants may be required to appear onsite at a Wolters Kluwer office as part of the recruitment process.
Compensation:
$118,300.00 - $207,400.00 USDThis role is eligible for Bonus.
Compensation range listed is based on primary location of the position. Actual base salary offer is influenced by a wide array of factors including but not limited to skills, experience and actual hiring location. Your recruiter can share more information about the specific offer for the job location during the hiring process.
Additional Information:
Wolters Kluwer offers a wide variety of competitive benefits and programs to help meet your needs and balance your work and personal life, including but not limited to: Medical, Dental, & Vision Plans, 401(k), FSA/HSA, Commuter Benefits, Tuition Assistance Plan, Vacation and Sick Time, and Paid Parental Leave. Full details of our benefits are available upon request.
How we rate this
Lead Technology Product Manager, AI Quality & Evaluation at Wolters Kluwer rates 72 out of 100 for how much of the daily work is AI. That makes it Works on AI (AI Level 3 of 4). The level is about AI in the job, not seniority.
Works on AI. The daily work is on AI products, without building the model.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.
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A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.
Skills and AI tools this role asks for
Questions you could be asked
- How do you decide that one model's output is better than another's for a given task?
- How do you think about the risk of an AI system in this kind of role failing silently?
- Describe a typical day in a role like this one: which parts run through AI directly?
- If you removed AI from this role, what would be left, and how do you decide what still needs a human?
Adapt your resume
- List these exact terms on your resume: AI Evaluation and AI Safety. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
- Show where AI is part of your daily process, not a one-off project — this role expects it to be a running habit.
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