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About Abby Care: Powering the future of care at home for all of America.
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Abby Care is building the leading AI\-native platform for family\-led care. America is facing a growing care crisis. Millions more people need care at home than ever. Over 50 million family caregivers support loved ones without the tools, training, or recognition they deserve.
We believe families are the largest untapped caregiving workforce in America, and that technology can help them deliver better care while driving stronger outcomes and greater transparency across the healthcare system.
Abby Care combines clinical oversight with an AI\-powered platform to train, enable, and support family caregivers in delivering high\-quality care at home. Our platform helps health plans and government partners better understand, verify, and improve care in the home. We expand access to care, reduce reliance on higher\-cost settings, and help ensure public dollars are spent effectively.
We are proud to partner with leading health plans, providers, and community organizations and are backed by top VCs. We envision a future where family\-led care is a core part of the healthcare system. Abby Care is building that future.
Join us in solving one of the most important challenges of our time.
The Role
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We’re looking for a high\-ownership, execution\-driven Senior Product Manager to build the next generation of clinical and care delivery systems at Abby Care. You’ll own the product experience for RN case managers, caregivers, and clinical operations teams who deliver care to our families.This is a builder role for someone who thrives in ambiguity and regulated environments, calculates trade\-offs, moves with urgency, and drives momentum across engineering, clinical, and operations teams. You’ll partner closely with our clinical leadership to build products that improve care quality, clinical outcomes, and operational efficiency.
You’ll own complex, clinically critical product areas end\-to\-end including personalized care planning, clinical assessment automation, proactive care adjustments, care team coordination, quality assurance, and clinical decision support.
*(Please note: While you will be driving these clinical initiatives, prior clinical AI product experience is a plus, not a strict requirement. We are looking for strong product fundamentals and a desire to dive deep into clinical workflows.)*
This is a full\-time opportunity based in San Francisco, CA.
Key Responsibilities
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- Build AI\-Native Clinical Products: Lead the strategic design, iteration, and execution of AI\-powered products focused specifically on care plan personalization, clinical assessment automation, and proactive clinical interventions.
- Own End\-to\-End Product Execution: Take full ownership of complex, clinically critical product areas, from problem discovery and PRDs through delivery, launch, and iteration. Be accountable for real\-world clinical outcomes and care delivery quality.
- Translate Real\-World Clinical Workflows: Partner deeply with clinicians, caregivers, and compliance teams to understand how care is delivered today, transforming those clinical workflows into intuitive, reliable, and scalable product experiences.
- Innovate Alongside the Care Ecosystem: Collaborate with providers, payors, regulators, and internal clinical leaders to unlock new models of care delivery. Focus heavily on developing systems where AI models evaluate care delivery against established clinical protocols.
- Raise the Bar for Product Thinking: Act as a senior leader in setting a high standard for product clarity, judgment, and craft. Mentor peers, inspire cross\-functional collaboration, and continuously elevate how product decisions are made and shipped across EPD.
The Requirements
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- 6\+ years of professional experience as a Product Manager.
- AI Product Expertise: Strong track record building and shipping AI\-powered products, including experience with LLMs, generative AI, or intelligent automation systems.
- End\-to\-End Ownership: Demonstrated history of owning, shipping, and scaling highly impactful products end\-to\-end across web and mobile.
- Execution \& Communication: Strong ability to distill complex, scattered inputs into clear, well\-prioritized roadmaps and requirements. Excellent written and verbal communication, including producing clear, structured, and actionable PRDs.
- Domain Background: Experience building complex workflow products or data\-heavy systems in healthcare, highly regulated environments, or fast\-moving startups.
Bonus / Nice to Have
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- Clinical AI Product Experience: Proven experience building AI\-powered products specifically for clinical care delivery, care plan development, or clinical decision support.
- Clinical Protocol Expertise: Direct experience with clinical product development where AI models are used to evaluate care delivery against clinical protocols.
- Healthcare Domain Knowledge: Understanding of clinical workflows, EMR systems, care management, or healthcare operations.
- Regulated Environment Expertise: Deep experience navigating HIPAA, payer requirements, or other healthcare compliance frameworks.
Benefits:
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- Competitive compensation packages that reflect the value you bring. We reward our team for the impact of their work.
- Comprehensive health coverage that works for you. Choose from high\-quality medical dental and vision options, including a $0 deductible PPO and a company\-funded HSA, alongside employer\-paid life and disability insurance.
- Generous paid time off. We provide policies that allow you to recharge along with 10 paid company holidays.
- Financial savings benefits to support your future. We support your financial well\-being with HSA contributions, optional FSA and commuter benefits, and full coverage of all 401(k) account fees (employer match not currently offered).
- Paid parental leave to support your growing family. We provide up to 10 weeks of paid parental leave based on tenure, so you can focus on bonding and adjusting to life as your family grows.
Our Values
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- Families First
Redefining healthcare starts with how we treat the parents and children we serve. We go above and beyond for every family, building strong, lasting relationships. We continually ask ourselves, *“Would we want this for our own families?”*
- Urgency with Precision
Millions of families are waiting for care, and they cannot wait, therefore this is not your typical 9 to 5 job. We match their urgency with our own, delivering exceptional care without compromise. Here, speed and excellence go hand in hand.
- Relentlessly Resourceful
As an ambitious startup, we adapt quickly and make the most of limited time and resources. We solve challenges with creativity to deliver results without unnecessary complexity.
- Purpose with Positivity
We take our mission seriously while never losing sight of the people behind the work. Respect, kindness, memes, and coffee make us stronger as a team and better for the families we serve.
- Driven to Redefine What’s Possible
We are here to make healthcare better, which means asking hard questions, challenging outdated systems, and finding smarter, more compassionate ways to deliver care.
*Automated Decision Tools*
*Abby Care may use automated decision tools to help match and rank candidate work experience, education and skills found in online profiles, resumes and job applications against job requirements. We believe that these tools help ensure objective, data\-based decisions during the hiring process, however, all Abby Care hiring decisions involve final human review and input. If you have questions or would like to request an alternative process, contact* *[email protected]**.*
Compensation Range: $170K \- $230K
Salary Context
This $170K-$230K 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 Abby Care, 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 ($200K) sits 8% below the category median. Disclosed range: $170K to $230K.
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.
Abby Care AI Hiring
Abby Care has 1 open AI role right now. They're hiring across AI Product Manager. Based in San Francisco, CA, US. Compensation range: $230K - $230K.
Location Context
AI roles in San Francisco pay a median of $265,000 across 1,335 tracked positions. That's 23% above 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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