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Build trusted AI experiences that help clinicians getfrom question to answerfaster.
At Wolters Kluwer Health, the UpToDate suite supports millions of clinical decisions every day. As AI changes how clinicians search for, interpret, and apply clinical knowledge, we are building product experiences that make trusted guidance easier to access, understand, and use.
As Senior Product Manager, AI Clinical Decision Support, you will play a core role in developing AI\-enabled experiences for UpToDate's core clinical decision support products. Working under product leadership, you will own discovery and delivery for defined capabilities that improve how users ask clinical questions, navigate evidence\-based content, and engage with trusted answers.
This role focuses on building useful, responsible, and engaging AI product experiences that support clinical judgment and strengthen the core UpToDate experience.
What You'll Do
- Drive discovery and delivery for defined AI\-enabled clinical decision support capabilities.
- Translate clinician, customer, and business needs into product requirements, experiments, roadmap priorities, and measurable outcomes.
- Shape experiences that help clinicians ask questions, find relevant information, understand clinical context, and move from question to action with confidence.
- Partner with engineering, design, clinical, content, analytics, customer\-facing, and commercial teams from discovery through launch and iteration.
- Define success metrics tied to adoption, engagement, trust, user satisfaction, workflow impact, and business value.
- Use customer conversations, concept tests, analytics, and feedback to validate user needs and product direction.
- Make product decisions that balance customer value, usability, clinical trust, technical feasibility, speed, cost, and responsible AI considerations.
- Communicate roadmap priorities, rationale, risks, and tradeoffs clearly across teams and leadership.
What You'll Bring
- 5\+ years of product management experience in healthcare, enterprise SaaS, content products, search, workflow, or data\-driven products.
- Strong product discovery and execution skills, including customer research, experimentation, roadmap development, and metrics\-driven decision\-making.
- Experience translating complex customer problems into intuitive product experiences, clear requirements, and measurable outcomes.
- Ability to partner closely with engineering, design, clinical, content, analytics, commercial, and customer\-facing teams.
- Strong communication, analytical thinking, stakeholder\-management, and influencing skills.
- Curiosity and learning agility around AI, automation, emerging technologies, and responsible product development.
- Ability to operate in ambiguity and make sound product decisions in complex, regulated healthcare environments.
Preferred Background
- Experience with AI\-enabled, data\-driven, search, content, knowledge, or decision\-support products.
- Practical understanding of generative AI, retrieval\-based systems, recommendation systems, workflow automation, or applied machine learning concepts.
- Experience with clinical decision support, healthcare content, point\-of\-care tools, or professional\-information products.
- Familiarity with human\-in\-the\-loop workflows, responsible AI practices, model evaluation, or trust and safety considerations.
What Success Looks Like
- AI\-enabled CDS experiences help users get to relevant, trusted information faster.
- New capabilities improve adoption, engagement, user satisfaction, and measurable customer value.
- Product teams have clear priorities, validated learnings, and actionable success metrics.
- AI experiences are designed with appropriate transparency, evidence grounding, and responsible use practices.
Our Interview Practices
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*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 USD
This 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:
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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.
Salary Context
This $118K-$207K range is below the median for AI Product Manager roles in our dataset (median: $185K across 167 roles with salary data).
View full AI Product Manager salary data →Role Details
About This Role
AI Product Managers define what AI features get built and why. They translate business problems into ML-solvable tasks, work with engineering to scope model requirements, and own the metrics that determine if an AI feature is working. The role requires a rare combination of technical fluency and product instinct.
Unlike traditional product management, AI PM work involves managing uncertainty at a fundamental level. Your model might work 90% of the time. What happens the other 10%? What's the user experience when the AI is wrong? How do you measure 'good enough' for a probabilistic system? These questions don't have easy answers, and the AI PM is the person responsible for finding them.
Across the 4,317 AI roles we're tracking, AI Product Manager positions make up 4% of the market. At Wolters Kluwer, this role fits into their broader AI and engineering organization.
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
What the Work Looks Like
A typical week includes: reviewing model evaluation results with the ML team, defining success metrics for a new AI feature, conducting user research on how customers respond to AI-generated outputs, writing product requirements that include accuracy thresholds and fallback behaviors, and presenting the AI roadmap to leadership. You're the translator between technical capability and business value.
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
Skills in Demand for This Role
Technical fluency with ML concepts is essential, though you won't be writing models. Expect to understand training data, evaluation metrics, model limitations, and responsible AI practices. SQL and basic Python are increasingly expected. Experience with A/B testing, data analysis, and product analytics is baseline. Understanding LLM capabilities and limitations is now a core requirement.
The differentiator is AI-specific product thinking: knowing when to use ML vs. heuristics, understanding the cost of training data collection, designing graceful degradation for model failures, and building products that improve with usage data. Experience with AI safety, bias mitigation, and responsible AI deployment is increasingly important.
Strong postings describe specific AI products the PM will own, mention the ML team structure, and talk about measurement methodology. Look for companies that have already shipped AI features. Roles at companies that are 'exploring AI' often mean you'll spend a year defining the strategy before any building happens.
Compensation Benchmarks
AI Product Manager roles pay a median of $217,100 based on 471 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($162K) sits 25% below the category median. Disclosed range: $118K to $207K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Wolters Kluwer AI Hiring
Wolters Kluwer has 5 open AI roles right now. They're hiring across AI Product Manager, AI/ML Engineer. Positions span Coppell, TX, US, Chicago, IL, US, Kennesaw, GA, US. Compensation range: $188K - $298K.
Location Context
AI roles in Chicago pay a median of $192,900 across 197 tracked positions. That's 10% below the national median.
Career Path
Common paths into AI Product Manager roles include Product Manager, Data Analyst, Technical Program Manager.
From here, career progression typically leads toward Director of AI Product, VP Product, Head of AI.
The most effective path is PM experience plus self-directed AI education. Take Andrew Ng's courses, build a small ML project, and learn enough Python to read model evaluation code. The goal isn't to become an ML engineer. It's to have credibility in technical conversations and to understand what's possible, what's hard, and what's a bad idea.
What to Expect in Interviews
AI interviews typically combine coding challenges (Python-focused), system design questions tailored to the role, and discussions about your experience with relevant tools and frameworks. Strong candidates demonstrate both technical depth and the ability to make pragmatic engineering tradeoffs. Prepare portfolio projects that demonstrate end-to-end capability rather than isolated skills.
When evaluating opportunities: Strong postings describe specific AI products the PM will own, mention the ML team structure, and talk about measurement methodology. Look for companies that have already shipped AI features. Roles at companies that are 'exploring AI' often mean you'll spend a year defining the strategy before any building happens.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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