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About the Role
Caregiving is broken. Families facing one of the hardest decisions of their lives are left to navigate it alone, with little clarity on what their loved one needs or where to find it. A Place for Mom is building the AI-powered platform to change that, and to step-change the industry standard for finding the right senior care option for loved ones. This role is a pivotal part of that equation: connecting families, through advisors, to the providers who can care for them.
We are looking for a Senior Product Manager to own how A Place for Mom advisors reach families across phone, SMS, and chat, and the intelligence built on those conversations, from the first call through to the move.
You will design and ship the systems advisors rely on in every conversation: the channels they use to connect, the live transcript those conversations produce, and the real-time coaching and insights built on top of it. You will partner across Product, Engineering, Data, Sales, Sales Operations, and Compliance, and own measurable outcomes in connection rate, advisor efficiency, and move-in rate.
This is an AI-native product role with deep technical fluency, not a telephony role with AI seasoning. If you use AI to do your own best work every day, think at the architecture level, and want to turn every family conversation into an input to the product, this is for you.
What You'll Own
Communication channels
- Be the product owner for our telephony systems and every advisor-to-family communication channel: outbound and inbound calling, SMS, advisor chat, and follow-up email
- Own the product strategy and roadmap for how advisors reach families across phone, SMS, and chat, and how the right channel gets chosen for each family
- Lead the product side of our move to a next-generation contact center platform: evaluation, migration, and keeping today's platform stable and current until cutover
The live transcript
- Treat the live transcript as a product: define the contract that real-time coaching, call scoring, the family's care profile, and advisor summaries all build on
- Balance what each consumer needs, from real-time delivery during the call to complete post-call records to structured data pulled from what the family said
Copilot and conversation intelligence
- Build real-time, in-app coaching and insights that guide advisors during the call, not after it
- Build call scoring and coaching dashboards that give sales managers clear coaching opportunities, scored off our own transcripts inside our own product
Follow-up and nurture
- Define where leads go after referral, whether they stay with their current advisor or move to a dedicated follow-up advisor, and how they are prioritized
- Own the treatment each family receives: the mix and cadence of calls, SMS, and email, and the outreach strategy that keeps families engaged through a long decision
Compliance and partner integrations
- Build do-not-call and consent requirements into every channel from the start, so families' stated preferences are honored everywhere
- Partner with our provider-facing teams to deliver live transfer to senior care partners across our network
Measurement and experimentation
- Define metrics, baselines, and targets for connection rate, calls per family, advisor coaching behaviors, and move-in rate; set hypotheses and lead learning agendas
- Instrument every launch so we know what moved the outcome, and translate what we learn into clear investment recommendations
What Success Looks Like in 12 Months
- The next-generation contact center platform is live for advisors, and live transcription is flowing into the product as a defined contract other teams build on
- Real-time in-call coaching is live for a pilot group of advisors, with a measurable change in the call behaviors that separate top performers
- Sales managers are coaching from call scoring and dashboards built on our own transcripts
- Connection rate is up and calls per family are down, while move-in rate holds or improves
- A follow-up and nurture model covering routing, prioritization, and cadence across call, SMS, and email is live and attributable to move-in rate
- Compliance requirements are built into every channel, with no regressions through the platform migration
What You Bring
- 5–8+ years of experience in product management, including shipping real-time, conversational, or communications products such as contact center, telephony, messaging, or voice
- AI-native in how you work, not just in what you build: you use LLMs and agents every day to research, prototype, analyze, and ship, running work in parallel and putting interactive prototypes in front of people instead of decks
- Hands-on experience applying LLMs to real data: you connect models directly to data sources to explore on your own, and you have used them to analyze conversations at scale, defining the signals to extract before you run the analysis
- Sound judgment on where AI belongs: you know when a deterministic system beats an agentic one, especially where attribution matters, and how to give a deterministic flow an agentic feel
- Technical fluency at the architecture level, including APIs, event streams, data models, identity resolution, and real-time transcription pipelines, and the ability to shape sequencing around technical constraints and hard dependencies
- A habit of starting with a quantifiable wedge, not the whole vision, and building reusable primitives that outlast the platform they launch on
- Comfort acting on directional signal while being explicit about confidence, paired with the rigor to validate at scale before you commit
- Proven ability to write clear requirements, run experiments, partner closely with engineering, and own delivery end-to-end
- Experience aligning sales leadership, operations, compliance, and engineering around a shared roadmap
- Bonus: contact center platform migrations, speech and transcription technology, conversation intelligence or call scoring, TCPA or do-not-call compliance, marketplace or health-adjacent domains
Why You'll Love It Here
- Mission-driven work that helps families make meaningful, high-stakes decisions
- The opportunity to shape how AI shows up in every advisor conversation at a pivotal moment for our advisor platform
- A role that combines communications infrastructure, conversation intelligence, and AI-native product building, three skill sets that rarely sit in one seat
- High-leverage influence across advisors, sales leadership, and cross-functional partners
- The chance to turn every family conversation into an input to the product, and to build the coaching that no competitor can buy
Compensation
Base Salary: $165,000 – $195,000
Bonus: 10% Corporate Bonus
Benefits:
- 401(k) plus match
- Dental insurance
- Health insurance
- Vision Insurance
- Paid Time Off
About A Place for Mom
A Place for Mom is the leading platform guiding families through every stage of the aging journey. Together, we simplify the senior care search with free, personalized support — connecting caregivers and their loved ones to vetted providers from our network of 15,000+ senior living communities and home care agencies.
Since 2000, our teams have helped millions of families find care that fits their needs. Behind every referral and resource is a shared goal: to help families focus on what matters most — their love for each other.
We’re proud to be a mission-driven company where every role contributes to improving lives. Caring isn’t just a core value — it’s who we are. Whether you’re supporting families directly or driving innovation behind the scenes, your work at A Place for Mom makes a real difference.
Our employees live the company values every day:
- Mission Over Me: We find purpose in helping caregivers and their senior loved ones while approaching our work with empathy.
- Do Hard Things: We are energized by solving challenging problems and see it as an opportunity to grow.
- Drive Outcomes as a Team: We each own the outcome but can only achieve it as a team.
- Win The Right Way: We see organizational integrity as the foundation for how we operate.
- Embrace Change: We innovate and constantly evolve.
Additional Information:
A Place for Mom has recently become aware of the fraudulent use of our name on job postings and via recruiting emails that are illegitimate and not in any way associated with us. APFM will never ask you to provide sensitive personal information as part of the recruiting process, such as your social security number; send you any unsolicited job offers or employment contracts; require any fees, payments, or access to financial accounts; and/or extend an offer without conducting an interview.
If you suspect you are being scammed or have been scammed online, you may report the crime to the Federal Bureau of Investigation and obtain more information regarding online scams at the Federal Trade Commission.
All your information will be kept confidential according to EEO guidelines.
A Place for Mom uses E-Verify to confirm the employment eligibility of all newly hired employees. To learn more about E-Verify, including your rights and responsibilities, please visit www.dhs.gov/E-Verify.
Compensation Range: $165K - $195K
Salary Context
This $165K-$195K range is below the median for AI Product Manager roles in our dataset (median: $194K across 123 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 9,079 AI roles we're tracking, AI Product Manager positions make up 5% of the market. At A Place for Mom, 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 947 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300. This role's midpoint ($180K) sits 18% below the category median. Disclosed range: $165K to $195K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
A Place for Mom AI Hiring
A Place for Mom has 1 open AI role right now. They're hiring across AI Product Manager. Based in US. Compensation range: $195K - $195K.
Location Context
AI roles in Austin pay a median of $209,100 across 306 tracked positions.
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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 Safety roles lead at $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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