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About This Role
Luxury Presence is building the AI growth platform for real estate. Backed by Bessemer Venture Partners and other top investors, we're a Series C company that has hit $100M in annual recurring revenue. More than 90,000 real estate professionals, including over 30% of the WSJ Real Trends top 100 agents in the United States, use us to run and grow their business.
Role Overview
We're looking for a Staff Product Manager to own how our clients get found in search and AI: more than 20,000 real estate websites, each competing in its own market, with a real business depending on the outcome. This is a platform product, where one good product decision reaches thousands of businesses at once.
Search is being rebuilt in real time. Our clients are evaluated by Google, by ChatGPT, Perplexity, and Gemini, and by every prospective seller who asks an assistant which agent they should call. The best people in this job design for three constituencies at once: the people who visit these sites, the crawlers and AI agents that read them, and the real estate business whose revenue depends on both.
This is a product leadership role. You'll run this area like a business: set the strategy, ship with your own engineering team, and work across design, client success, and sales to make it land. It is not a job for someone who runs audits and hands recommendations to other people. Most real estate professionals will never hire a technical SEO of their own. Our platform is how they get one.
What You'll Do
- Own the product strategy, roadmap, and results for organic discovery and AI visibility across every site on the platform, including what clients can see, control, and get credit for
- Spend real time with clients and the teams who serve them, and turn what you learn into a clear point of view on what to build next
- Build the business case for your roadmap and tie it to retention, revenue, and client outcomes
- Make our clients' brands, listings, and expertise legible to AI assistants, and define how we measure citations and visibility across answer engines
- Own the technical foundations every client site depends on, partnering with engineering and the team building our next-generation website platform so recurring problems get solved once through better defaults, automation, or safeguards
- Prove changes on representative groups of sites, establish real impact, then scale what works
- Ship with your own engineering and design partners, writing requirements clear enough that the team moves without waiting on you
- Partner with client success, sales, and marketing so launches land and clients understand the value we deliver
Who You Are
- 7+ years of product management, including owning a product line end to end where you were accountable for adoption, client outcomes, and revenue
- You run your area like a business. You build the case before you build the product, tie your roadmap to retention and revenue, and own the result
- Close to your customers. You talk to them often, understand their business, and can explain why something matters in their terms, not just in rankings
- You create momentum in ambiguity. You narrow the problem, make a call, and move before every question is answered
- Strong opinions, honestly tested. You form a point of view, go looking for the counterargument, and change your mind when the evidence says so
- You make complex things simple for non-technical users, favoring smart defaults and guardrails over configuration
- A communicator who can get a room excited, from engineers to executives to client success, and who earns trust across teams you don't manage
- Hands-on experience with SEO or organic growth and a current point of view on AI search, with enough technical depth to be credible with engineers and to know which details actually move results
- Rigorous about measurement. You test before you scale and can tell a result from a coincidence
- Fluent with AI as a working tool, using it to investigate, prototype, and unblock yourself
Preferred Qualifications
- Product experience at a company serving small businesses or solo operators, where customers are busy, non-technical, and judge you on results, such as Shopify, Squarespace, Mailchimp, Square, Yelp, or Thumbtack
- You've owned a marketing product that customers use to grow their own business, such as websites, email, local listings, reviews, or social
- You've made slow or uncertain results visible and credible to customers, in a category like SEO or advertising where impact takes months to show up
- You've worked on a platform where a single change reaches many customers' sites or accounts at once
- You've shipped LLM-powered products to production, especially ones that generate content
- A background in content, editorial, or brand work, and a feel for how a business's voice should change depending on who it's trying to reach
- Founder, GM, or P&L experience
Join us in shaping the future of real estate
The real estate industry is in the midst of a seismic shift, and the future belongs to those who break new ground. As one of the fastest-growing companies in the proptech and marketing sectors, Luxury Presence challenges the status quo of what technology can do for real estate agents, leaders, and brokerages.
We're a team of agile and tenacious innovators working collaboratively to drive the industry forward. Together, we build game-changing products that empower modern real estate entrepreneurs to dominate their markets. From award-winning web design to agile SEO solutions to cutting-edge AI tools, we deliver tech that anticipates market shifts and keeps our clients ahead of their competition.
Founded in 2016 by Stanford Business School alum Malte Kramer, Luxury Presence has grown to a global team ranked on the Inc. 5000 fastest-growing companies list three years in a row. We're backed by world-class investors, including Bessemer Venture Partners, NextEquity Partners, Toba Capital, and Switch Ventures, and have raised $89 million to date.
More than 18,000 real estate businesses rely on our platform, including 30% of the Wall Street Journal RealTrends top agents and teams. Additionally, many of the industry's most powerful brokerages rely on Luxury Presence as a trusted business partner.
Every year since 2020, Luxury Presence has ranked on BuiltIn's Best Place to Work lists. HousingWire named our founder and CEO a 2024 Tech Trendsetter, we've received several Tech100 Awards, and we just scored an Inman Innovation Award for Best AI-Powered Platform.
Luxury Presence is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, or national origin.
Recruitment Notice: Luxury Presence employees will only contact candidates from an @luxurypresence.com email address. We may work with trusted recruiting partners who will contact you from their company's email domain. If you have questions about the authenticity of a recruiting communication, please contact [email protected]. We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
Salary Context
This $170K-$200K range is below the median for AI Product Manager roles in our dataset (median: $187K across 155 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 11,520 AI roles we're tracking, AI Product Manager positions make up 4% of the market. At Luxury Presence, 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 Required
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 993 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($185K) sits 16% below the category median. Disclosed range: $170K to $200K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
Luxury Presence AI Hiring
Luxury Presence has 1 open AI role right now. They're hiring across AI Product Manager. Based in Remote, US. Compensation range: $200K - $200K.
Remote Work Context
Remote AI roles pay a median of $188,200 across 2,430 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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, 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 $266,050 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 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.
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