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Recognized as the No. 1 site trusted by real estate professionals, Realtor.com® has been at the forefront of online real estate for over 25 years, connecting buyers, sellers, and renters with trusted insights and expert guidance to find their perfect home. Through its robust suite of tools, Realtor.com® not only makes a significant impact on the real estate industry at large, but for consumers, navigating the biggest purchase they will make in their life, by providing a user experience that is easy to use, easy to understand, and most of all, easy to make decisions.
Join us on our mission to empower more people to find their way home by breaking barriers to entry, making the right connections, and building confidence through expert guidance.
Are you passionate about the intersection of AI, data, and client experience? Do you get excited about the moment a new customer goes from signed contract to first value and want to engineer that journey with precision? We’re looking for a product leader who thinks in systems, ships with velocity, and brings an AI\-first mindset to every problem.
As the Lead Product Manager, AI Activation , you will own the end\-to\-end client activation experience, the critical window between contract close and a Client’s first meaningful outcome with Realtor.com®. This is a high\-visibility, technically rigorous role at the core of our Client Experience team, owning the platform capabilities, AI\-powered experiences, and shared services that help Brokers, Teams, and Agents go live faster, set up with confidence, and realize value sooner.
What You’ll Do:
As the primary product leader for AI Activation, you will define the strategy, roadmap, and delivery cadence that drives client time\-to\-value and platform efficiency across the activation lifecycle.
*AI\-Driven Activation Strategy*
- Define and own the product vision for AI\-powered activation, from automated onboarding workflows to intelligent setup recommendations that reduce time\-to\-first\-value for new Clients.
- Develop and maintain a roadmap that balances near\-term activation wins with long\-term platform scalability and AI capability investment.
- Identify and prioritize opportunities to embed AI/ML throughout the activation journey, including predictive setup guidance, anomaly detection, and personalized onboarding flows.
*Seamless Onboarding \& Time\-to\-Value*
- Own enterprise onboarding capabilities end\-to\-end: roster ingestion, identity and access provisioning, product setup and configuration, and team hierarchy management.
- Drive measurable reduction in activation time by identifying and eliminating friction across the self\-serve and assisted setup paths.
- Partner with Go\-to\-Market and Customer Success to align product setup milestones with commercial success signals: first login, first lead, first renewal touchpoint.
*Platform Capabilities \& Technical Depth*
- Guide the development of foundational, reusable activation services, including SSO/third\-party integrations, data provisioning, and configuration APIs that other product teams build on.
- Own Client data management across the RDC ecosystem, ensuring consistency, accuracy, and accessibility of client account structures, agent\-team\-broker hierarchies, and entitlement data.
- Collaborate closely with Engineering and Data Science to define system architectures, data models, and entity relationships that support complex account structures at scale.
*Analytics, Instrumentation \& Insight*
- Define and own the activation funnel metrics: setup completion rates, time\-to\-activate, drop\-off points, and downstream correlation to retention and engagement.
- Partner with Data Science to build predictive models that flag at\-risk activations early, enabling proactive intervention before churn risk compounds.
- Use qualitative and quantitative signals, session data, support tickets, NPS, sales feedback, to continuously validate and refine the activation experience.
*Cross\-Functional Alignment \& Stakeholder Influence*
- Serve as the primary product voice for AI Activation to Revenue, Engineering, Design, Data Science, and Go\-to\-Market stakeholders.
- Translate complex technical strategies and AI capabilities into clear, business\-outcome\-driven narratives for exec audiences.
- Collaborate with Engage and Grow \& Renew teams to ensure a seamless handoff from activation to ongoing value delivery.
What You’ll Bring:
- Product Leadership: \~10 years in product management, with meaningful time in B2B/SaaS, marketplace, or platform environments. Experience owning onboarding, activation, or lifecycle products is a strong plus.
- Platform \& Technical Depth: Strong technical background in platform and API products, shared services, data provisioning, identity/access systems, or developer\-facing capabilities. You can read an ERD, engage in architecture reviews, and hold your own in technical design discussions.
- Data Modeling \& Structural Thinking: Experience shaping data models, entity hierarchies, and account structures for complex B2B clients. You understand how team\-broker\-agent relationships compound at scale.
- AI\-First Product Thinking: Fluency with AI/ML concepts as they apply to product: recommendation systems, predictive modeling, anomaly detection, LLM\-powered workflows. You don’t need to build the models — but you need to know when to use them, how to spec them, and how to evaluate them.
- Analytical Rigor: Proven ability to define activation and onboarding funnels, instrument them correctly, identify failure modes, and drive measurable improvement.
- B2B Client Empathy: You know how professional partners set up, manage, and scale their business — and how product can remove the friction that slows that process down.
- Strategic Communication: You can run a sprint review with engineers in the morning and present a roadmap to a CRO in the afternoon. Both audiences walk away with clarity.
- Commitment to Inclusion: A desire to work within a diverse team where empathy, intellectual curiosity, and collaboration are as valued as technical skill.
- Bachelor’s Degree: In related field (such as computer science, business, etc.) or equivalent experience.
How We Work:
We balance creativity and innovation on a foundation of in\-person collaboration. For most roles, our employees work three or more days in our offices, where they have the opportunity to collaborate in\-person, adding richness to our culture and knitting us closer together.
How We Reward You:
Realtor.com is committed to investing in the health and wellbeing of our employees and their families. Our benefits programs include, but are not limited to:
- Inclusive and Competitive medical, Rx, dental, and vision coverage
- Family forming benefits
- 13 Paid Holidays
- Flexible Time Off
- 8 hours of paid Volunteer Time off
- Immediate eligibility into Company 401(k) plan with 3\.5% company match
- Tuition Reimbursement program for degreed and non\-degreed programs
- 1:1 personalized Financial Planning Sessions
- Student Debt Retirement Savings Match program
- Free snacks and refreshments in each office location
Do the best work of your life at Realtor.com®
Here, you’ll partner with a diverse team of experts as you use leading\-edge tech to empower everyone to meet a crucial goal: finding their way home. And you’ll find your way home too. At Realtor.com®, you’ll bring your full self to work as you innovate with speed, serve our consumers, and champion your teammates. In return, we’ll provide you with a warm, welcoming, and inclusive culture; intellectual challenges; and the development opportunities you need to grow.
Diversity is important to us, therefore, Realtor.com® is an Equal Opportunity Employer regardless of age, color, national origin, race, religion, creed, gender, sex, sexual orientation, gender identity and/or expression, marital status, status as a disabled veteran and/or veteran of the Vietnam Era or any other characteristic protected by federal, state or local law. In addition, Realtor.com® will provide reasonable accommodations for otherwise qualified disabled individuals.
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 3,708 AI roles we're tracking, AI Product Manager positions make up 5% of the market. At News Corp, 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 $216,175 based on 270 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
News Corp AI Hiring
News Corp has 12 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist, AI Product Manager. Positions span New York, NY, US, Austin, TX, US. Compensation range: $95K - $270K.
Location Context
AI roles in Austin pay a median of $214,343 across 87 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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