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CANDIDATES MUST BE LOCATED IN THE UNITED STATES OR CANADA
About SafelyYou
SafelyYou’s passionate mission is to empower safer, more person-centered care across senior living through world-leading AI, industry-changing hardware, and remote expert clinicians, significantly improving outcomes for residents while increasing peace of mind for families and reducing costs for communities.
Originating in 2015 as the doctoral research of CEO George Netscher—and inspired by his own family's experience with Alzheimer's disease—SafelyYou was spun out of UC Berkeley’s Artificial Intelligence Research Lab, one of the top five AI research groups in the world. And today, our company is solving critical challenges in senior living, from resident falls and ER visits to staffing concerns, length of stay, and NOI. All helping ensure that communities can focus on improved care for residents while still reaching their financial goals.
SafelyYou is one of five most innovative fall technologies referenced in the Senate Falls Report (2019), a winner of the McKnight’s Tech Partner of the Year, and has been named to Fortune’s Impact 20 list, which recognizes companies making people’s lives better through innovation.
Your Role at SafelyYou
SafelyYou's AI+Human-In_Loop solutions (fall detection, mobility, severity tagging, presence, and change detection) powers every product we sell. You own the AI capability roadmap as a platform: the reusable models and signals our customer-facing teams turn into outcomes, and the path by which research becomes production value. You'll ship existing models into production in short cycles, help place a few longer research bets, and keep SafelyYou ahead as AI techniques change fast. AI research leadership sets research direction and customer-facing PMs own customer outcomes; you own what the AI can do, how well, and at what cost.
Key Responsibilities:
The Numbers You'll Own
- Readiness. Every model release passes an AI-readiness gate (accuracy, false alarms, review cost, rollback) within the next year.
- Quality and cost. A measurable reduction in with no loss in detection.
- Leverage. At least one capability reused by two or more product teams, and one research bet taken to a clear continue-or-stop decision.
What You'll Own
- One capability roadmap across edge and cloud, ranked by cross-team leverage.
- How AI is consumed: outputs, confidence thresholds, and latency and accuracy expectations.
- Model readiness and field performance: criteria from research to shadow deployment to production, then precision, recall, and drift in live communities.
- Human review economics: a feedback loop with our clinical reviewers that improves models and reduces review per event.
- Research to value: early-learning milestones for 6–12 month bets, plus data priorities (curation, edge cases, labeling).
- Emerging AI: track how newer techniques (vision-language and multimodal models, agentic systems, efficient on-device models) change what's possible, and turn the right ones into SafelyYou capabilities, such as richer event understanding and less human review.
- New sensing modalities with Hardware & Sensing: you own what models can extract; they own modality selection and cost.
The Ideal Candidate:
- The hard requirement: 7+ years of product management, including 3+ years leading ML or computer vision products you've taken to production and operated at scale.
- Fluency in modern AI/ML (computer vision, multimodal and vision-language models, evaluation), credible with researchers and ML engineers. Engineers who moved into product are welcome.
- Well versed in how newer AI techniques have emerged, from foundation and vision-language models to agentic systems and efficient on-device models, with a clear point of view on where they apply in care settings and where they don't.
- Experience deploying models under real-world constraints, such as on-device compute and latency versus accuracy.
- Evidence-driven: you design evaluations, run shadow deployments, and balance open-ended research with commercial commitments.
- Strongly preferred: human-in-the-loop systems, non-optical sensing, or safety-critical AI in healthcare.
Salary Range: $220,000 - $240,000 USD / $200,000 - $220,000 CAD commensurate with experience
Why Join Us? You Can Make a Difference!
SafelyYou offers a competitive compensation package, including but not limited to:
- A mission-driven company culture
- Fully remote
- Competitive salary & benefits, including fully paid employee premiums for Medical, Dental, and Vision
- 401k Program
- Monthly Education, Well-being & WFH stipends
- Non-accrual PTO
- Growth Potential
- Company Retreats
- Medical & Family/Parental Leave
At SafelyYou, we believe that a diverse, equitable, and inclusive workplace makes us a more relevant, competitive, and resilient company. We are committed to creating a diverse and inclusive workforce that celebrates diversity. We do not discriminate based on race, color, ancestry, religion, national origin, sexual orientation, age, citizenship, marital or family status, disability, gender identity or expression, veteran status, or any other legally protected status.
We value all the perspectives our team members bring to the table, and that is why we don’t look for culture fit, but culture add. We want to hear from YOU! We encourage you to apply even if you do not fit 100% of the technical requirements.
Salary Context
This $200K-$240K range is above the 75th percentile for AI Product Manager roles in our dataset (median: $193K across 93 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 8,943 AI roles we're tracking, AI Product Manager positions make up 5% of the market. At SafelyYou, 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 $220,200 based on 862 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. Disclosed range: $200K to $240K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
SafelyYou AI Hiring
SafelyYou has 1 open AI role right now. They're hiring across AI Product Manager. Based in Remote, US. Compensation range: $240K - $240K.
Remote Work Context
Remote AI roles pay a median of $185,906 across 2,124 positions. About 6% of all AI roles offer remote work.
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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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