Interested in this AI Product Manager role at Stealth HC?
Apply Now →About This Role
We are a small team obsessed with deep thinking, speed, craftsmanship, and a customer\-first approach.
Our mission is to build the best care experience in healthcare, with a 30\-year horizon. We are builders, physicians, and operators with deep experience across healthcare and technology. Our team brings more than 30 years of hands\-on experience building and operating independent practices, including urgent care and surgical centers.
We have also built and shaped products used by millions of people at companies including Meta, Dropbox, Instagram, Coda, and Nomad.
Collectively, our team has launched multiple zero\-to\-one products, helped scale companies beyond $1 billion in valuation, and grown products to more than 100 million monthly active users.
Today, we help physicians achieve independence by making it easier to start, operate, and grow their own practices.
We believe the future of healthcare will be shaped not by larger bureaucratic systems, but by empowered physicians. By lowering the barriers to practice ownership, we enable doctors to spend more time caring for patients, build enduring practices, and deliver a better healthcare experience.
We have reached $10M\+ ARR in under 14 months (placing us in the top decile for growth among companies at our stage), are profitable, and are backed by top\-tier healthcare investors.
### The role
This is a role for someone who wants real ownership, not just of a roadmap, but of outcomes. You’ll take products, workflows, and business\-critical problems from 1 to 100, shaping both what we build and how we scale it.
You’ll operate at every altitude. At 10,000 feet, you’ll define product direction, identify high\-leverage opportunities, and imagine how AI can fundamentally reinvent the way work gets done. At the one\-inch level, you’ll become deeply fluent in legal and medical workflows, dogfood the product, work directly with customers, and uncover the friction others miss.
You’ll be scrappy, hands\-on, and close to the work. You’ll turn real\-world insight into products people trust and rely on, while continuously testing, learning, and improving.
You’re a systems thinker with the imagination to design truly AI\-native experiences—and the operational discipline to build the processes, feedback loops, and systems that make those experiences better over time.
### What you’ll do
- Lead the end\-to\-end development of new 0 1 products, from problem definition through launch and iteration.
- Develop product strategy, define the roadmap, and make clear trade\-offs in fast\-moving, ambiguous environments.
- Partner closely with engineering and design to prototype quickly using foundation models, agents, retrieval systems, and other emerging AI capabilities.
- Translate insights into product requirements, user stories, success metrics, and launch plans.
- Design human\-in\-the\-loop experiences that combine AI capabilities with appropriate review, escalation, and operational controls.
- Build evaluation frameworks to measure product quality, model performance, reliability, safety, and business impact.
- Roll up your sleeves to be involved in the operations, improve processes, and close feedback loops.
- Establish the operating rhythms, measurement, and processes needed to scale successful products.
- Use data and qualitative feedback to prioritize opportunities and continuously improve the product.
### What you’ll bring
- 4\-9 years of product management experience, including experience launching a product or major product area from zero to one.
- Demonstrated ability to turn an ambiguous customer or business problem into a shipped product.
- Strong customer empathy and a bias toward action, learning, and iteration.
- Genuine interest in operations: willing to spend time in the field, learn workflows firsthand, and partner deeply with operational teams.
- Experience building with AI systems, or a strong track record of quickly developing technical fluency in emerging technologies.
- Ability to balance strategic thinking with executional detail.
- Strong communication and stakeholder\-management skills across technical and non\-technical teams.
- Comfort using data to make decisions, define success, and identify opportunities.
- A collaborative, low\-ego approach and willingness to do what is needed to move the work forward.
### Nice to have
- Experience in operationally complex environments such as healthcare, field services, and legal.
- Experience with pilots, service design, process improvement, or launching products with real\-world operational dependencies.
- Experience in AI and evaluation framework
- Startup or high\-growth company experience.
Compensation
- Base Salary: $140K\-$250K \+ equity (depending on experience and location)
- Equity: Competitive equity package in a well\-funded AI startup with significant upside
What We Offer
- Medical insurance through Blue Shield of California, with multiple PPO and HMO options
- Dental and vision insurance
- Unlimited PTO
- Hybrid work: three days per week in our San Francisco office, Tuesday–Thursday
- Lunch in the office
- Competitive equity package
- A high\-trust, low\-ego, high\-ownership culture where people move fast, and take real responsibility
Equal Opportunity Employer
We are an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. We do not discriminate based on race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, disability, or any other characteristic protected by law.
Salary Context
This $140K-$250K range is above 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 Stealth HC, 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. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($195K) sits 10% below the category median. Disclosed range: $140K to $250K.
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.
Stealth HC AI Hiring
Stealth HC has 1 open AI role right now. They're hiring across AI Product Manager. Based in San Mateo, CA, US. Compensation range: $250K - $250K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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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