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About This Role
Medscape, a division of WebMD, develops and hosts physician portals and related mobile applications that make it easier for physicians and healthcare professionals to access clinical reference sources, stay abreast of the latest clinical information, learn about new treatment options, earn continuing medical education credits and communicate with peers.
WebMD is an Equal Opportunity/Affirmative Action employer and does not discriminate on the basis of race, ancestry, color, religion, sex, gender, age, marital status, sexual orientation, gender identity, national origin, medical condition, disability, veterans status, or any other basis protected by law.
About Medscape
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At Medscape, a division of WebMD, our mission is to improve patient care by empowering healthcare professionals with trusted clinical knowledge, tools, and learning experiences.
We are transforming Medscape into a daily\-use, AI\-powered clinical companion, helping clinicians stay up to date, make better decisions, and continuously build their expertise. Across web and mobile, we deliver evidence\-based content, point\-of\-care tools, and interactive learning—now enhanced by AI to provide more contextual, personalized, and adaptive experiences, while maintaining the highest standards of clinical rigor and trust.
Role Overview
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We are looking for an AI Product Manager – Adaptive Learning to define and deliver AI\-powered learning experiences that help clinicians build knowledge and competency over time.
This role focuses on HCP\-facing experiences, including:
- Personalized learning journeys
- Adaptive content delivery
- AI\-powered recommendations and guidance
- Integration of learning within clinical workflows
Mission
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Shape the future of medical learning by leveraging AI to create personalized, continuous, and context\-aware learning experiences that improve clinician outcomes.
Key Responsibilities
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### Product Strategy \& Execution
- Define and drive the product vision and roadmap for AI\-powered adaptive learning experiences
- Identify opportunities to embed AI across learning journeys to improve engagement and outcomes
- Partner with engineering, data science, and design to deliver scalable solutions
### Discovery \& Prototyping
- Lead discovery to gather insights from customers, stakeholders, and data analysis to identify new product opportunities, specifically exploring how AI can solve problems and create value
- Take a hands\-on approach to rapidly prototype concepts using open\-source AI/GenAI tools
- Validate concepts through iterative testing and refinement
### Experience Design \& Personalization
- Design AI\-driven learning experiences that adapt to user context, behavior, and needs
- Define how content, tools, and recommendations are orchestrated across the user journey
- Ensure clinicians remain in control, with transparent and trustworthy AI interactions
### Collaboration \& Integration
- Work closely with:
- + AI/ML teams to leverage platform capabilities
+ Platform product lead to ensure reuse and scalability
+ Commercial teams to align learning experiences with broader offerings
- Contribute to shared platform evolution by ensuring learning needs are represented
### Internal AI Use Cases (Selective)
- Identify and support AI opportunities that enhance the structure, adaptability, and reusability of educational content
- Enable capabilities that improve how content is:
- + Structured for different learning contexts and time constraints
+ Adapted across formats and touchpoints
+ Aligned with personalization and progression models
- Ensure internal content workflows support more flexible, modular, and scalable learning experiences
Success Metrics
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- Engagement and retention in learning experiences
- Completion and participation in educational activities
- Improvement in learning outcomes and knowledge progression
- Adoption of AI\-powered learning features
Profile
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### Who You Are
- Bachelor’s degree in Computer Science or equivalent experience; strong understanding of LLMs and AI application architectures
- 5\+ years of product management experience, ideally in healthcare, digital education, or content platforms, with experience building and iterating AI\-powered products
- Strong understanding of digital learning experiences, content structuring, and engagement strategies, with an ability to design experiences that support knowledge progression over time
- Hands\-on mindset with the ability to prototype and experiment using AI/GenAI tools to validate ideas quickly
- Experience working with content systems (CMS), knowledge structures, or personalization frameworks
- Strong product discovery skills, with the ability to gather and synthesize insights from users, stakeholders, and data
- Data\-driven mindset with experience analyzing product performance and running experiments (e.g., A/B testing, engagement metrics)
- Comfortable working in cross\-functional Agile environments, partnering with engineering, design, data science, and content teams
- Strong communication and stakeholder alignment skills, including the ability to explain AI\-driven experiences clearly
- Ability to tackle complex, ambiguous problems with a structured and user\-centric approach
- Passion for improving healthcare professional education and learning outcomes
### Preferred Qualifications
- Experience with AI/ML domains such as LLMs, recommendation systems, or personalization engines
- Familiarity with learning management systems (LMS) or educational technologies
- Understanding of healthcare content ecosystems and editorial workflows
- Awareness of data privacy and ethical considerations in AI\-driven learning experiences
Reporting
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- Solid line: Head of Medscape Education Product
- Dotted line: Head of Medscape Education AI Strategy
Salary range: $125,000 \- $150,000
Bonus Eligible: This position is also eligible for a discretionary company bonus, based upon business results.
Benefits:
- Employees in this position are eligible to participate in the company sponsored benefit programs, including the following within the first 12 months of employment:
- Health Insurance (medical, dental, and vision coverage)
- Paid Time Off (including vacation, sick leave, and flexible holiday days)
- 401(k) Retirement Plan with employer matching
- Life and Disability Insurance
- Employee Assistance Program (EAP)
- Commuter and/or Transit Benefits (if applicable)
- *Eligibility for specific benefits may vary based on job classification, schedule (e.g., full\-time vs. part\-time), work location and length of employment.*
Salary Context
This $125K-$150K range is below the median for AI Product Manager roles in our dataset (median: $174K across 475 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 26,159 AI roles we're tracking, AI Product Manager positions make up 2% of the market. At WebMD, 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 Required
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 $204,600 based on 532 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $131,300. This role's midpoint ($137K) sits 33% below the category median. Disclosed range: $125K to $150K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
WebMD AI Hiring
WebMD has 2 open AI roles right now. They're hiring across AI Product Manager. Based in Newark, NJ, US. Compensation range: $150K - $150K.
Location Context
Across all AI roles, 7% (1,863 positions) offer remote work, while 24,200 require on-site attendance. Top AI hiring metros: Los Angeles (1,695 roles, $178,000 median); New York (1,670 roles, $200,000 median); San Francisco (1,059 roles, $244,000 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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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