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About This Role
Why We Need You – The Mission
Healthcare quality is complex. Medisolv helps make it manageable—and actionable.
We partner with hospitals, health systems, ACOs, and payers to bring clarity to quality data, connecting clinical and claims information into a single, reliable view. More than 1,800 organizations and 130M\+ patient records rely on Medisolv to measure, report, and improve performance across 500\+ measures tied to CMS, The Joint Commission, and other programs.
With support from Bessemer Venture Partners Forge, we’re continuing to grow our platform and expand our impact—investing in advanced analytics, AI, and new capabilities that help healthcare organizations stay ahead of change and improve outcomes with confidence. If you know the health insurance and VBC worlds inside out, understand the math behind CMS quality calculations, and you're genuinely excited about putting autonomous AI agents to work on messy healthcare data, we want you on the team.
What You’ll Accomplish – Your Performance Objectives
First 30 Days – Foundation \& Alignment
- Build a deep understanding of Medisolv’s data ecosystem and CMS measure logic (HEDIS, Pharmacy, CAHPS/HOS).
- Align with engineering, AI, and clinical stakeholders on priorities and high\-impact use cases.
- Define initial AI agent architecture and guiding principles for HITL safety and compliance.
First 3 Months – Design \& Prototype
- Design and prototype AI agents to identify care gaps and predict Star Rating risks.
- Stand up the foundation of the payer data core from fragmented datasets.
- Develop initial CMS measure calculation engines and pilot action\-oriented workflows.
First 6 Months – Launch \& Scale
- Deploy production\-ready AI agents that connect insights directly to gap\-closing actions.
- Expand and stabilize the payer data core and measurement automation.
- Implement orchestrated workflows and mature HITL guardrails.
First 12 Months – Optimize \& Drive Outcomes
- Optimize AI performance with feedback loops and expand to more advanced use cases.
- Mature the data platform into a scalable, real\-time decisioning engine.
- Drive measurable improvements in Star Ratings and value\-based care outcomes.
Who We’re Looking For \- The Competencies That Matter
- Payer \& VBC Domain Fluency: You have 5\+ years of product management experience handling health plan data and VBC provider quality metrics. You deeply understand risk adjustment, utilization management, and how risk\-bearing entities operate.
- CMS Quality \& Star Ratings Expertise: You know how CMS calculates MA Star Ratings inside out. You understand the math behind cut points, the impact of triple\-weighted measures, and how to build data products that move the needle across various CMS measure domains.
- Deep Comfort with Core Payer Data: You speak the language of payer operations. You have hands\-on experience working with claims, eligibility, and enrollment datasets, and you know how to map these back to quality performance.
- A "Beyond\-the\-Dashboard" Mindset: You are tired of building static BI tools. You want to build proactive software that thinks, adapts, and automates workflows rather than just reporting on them.
- AI \& Data Literacy: You don't need to write the code, but you must understand GenAI architectures, RAG, and agentic frameworks (like LangChain, AutoGen, or similar orchestration toolsets). You are comfortable talking to engineers about vector databases, APIs, and semantic data layers.
- Trust \& Security First: You understand that healthcare data requires extreme care. You know how to build within HIPAA/HITRUST guidelines and ensure AI outputs are accurate, unbiased, and explainable.
- You are a doer. You take initiative and enjoy driving tasks from inception to completion. You probably have a strong bias for action and may even become frustrated when things come to a stalemate. You use this frustration in a positive manner to drive towards a solution to move things forward.
- Collaborative. You have empathy for your colleagues. You demonstrate and influence cross\-functional collaboration within the company and seek out opportunities to build relationships with others even when difficult personalities or politics stood in the way.
- Flexibility. You understand that at growth stage companies, things will evolve, and you may have to be flexible in your approach and in your expectations. You are open\-minded and adapt well to changing environments as a company grows and scales.
- Growth Mindset. You love a challenge. You are intellectually curious and love to figure out how things work. You have a diverse set of interests inside and outside of work. You can articulate areas where you have worked hard on improving yourself over time.
- Resilient. You embrace change. You are optimistic. It’s not how many times you get knocked down, it’s how many times you get up.
How to be a Medisolver – Our Values
- Customer Success Obsession
- All\-Star Team Collaboration
- Continuous Improvement through Curiosity \& Data\-Driven Learning
- Courage with Kindness
- Execution Focus. We Do Business, Not Just Talk Business
*Medisolv is committed to creating a diverse and inclusive workplace. We believe that diversity drives innovation, and we are dedicated to fostering an environment where all employees feel valued and respected.*
*Candidates must successfully complete a pre\-employment background check and be legally authorized to work in the United States, as sponsorship is not available.*
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 3,823 AI roles we're tracking, AI Product Manager positions make up 5% of the market. At Medisolv Inc, 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 $213,800 based on 583 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Medisolv Inc AI Hiring
Medisolv Inc has 2 open AI roles right now. They're hiring across AI Product Manager, AI/ML Engineer. Positions span Clarksville, MD, US, US.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 $275,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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