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About This Role
FabFitFun is seeking a Senior Product Manager, AI and Internal Tools, to join our Product and Engineering team!
FabFitFun runs on dozens of operational workflows ripe for AI retooling. Buying, merchandising, inventory, billing, customer support, pricing, fraud, and CRM each have slow steps, manual handoffs, and tribal knowledge waiting to be rebuilt.
The role is AI\-first and starts on the inside. You'll work with operators, engineers, and leadership to find where AI changes the economics of a workflow and where it doesn't, then build the tools that prove the case. The same insights eventually shape what we ship to members, so the work runs from the warehouse to the app.
Location: This role is remote within the USA, with a preference for candidates based in the Los Angeles area.
What You’ll Do:
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- Collaborate closely with our internal team members to understand what they need, what's slowing them down, and where AI can make their work dramatically better
- Turn operator insights, company goals, team input, and the rapidly shifting AI landscape into a clear, focused roadmap that retools how FabFitFun runs
- Use what makes FabFitFun special to build internal tools no one else can, balancing proven AI playbooks with creative bets that make sense given our business
- Watch the AI tooling space and bring the best new patterns back to the team before competitors notice them
- Work closely with internal business units to ship simple, valuable internal tools through ruthless prioritization
- Phase out features and workflows that aren't pulling their weight, and prioritize what moves the P\&L
- Write tight specs and prototypes when needed, using AI tools to move from idea to working proof of concept faster than traditional documentation allows
- Help rebuild legacy workflows with a fresh, AI\-first mindset
- Build feedback loops into every AI feature so operators can flag bad outputs and the system gets sharper over time
- Keep everyone, from execs to operators to engineers, in the loop and excited about where AI is taking our operations and why it matters
- Work with design, engineering, and operations teams to turn rough ideas into real tools, designing for the full operator experience — including how humans stay in the loop when AI gets things wrong
- Evangelize AI across the company: run demos, teach operators how to use the tools well, and help shift the culture from "AI as a side project" to "AI as how we work"
- Mentor where you can, share what you learn, and help the team level up
What You’ll Bring:* 7\+ years in product management
- 4\+ years shipping e\-commerce, operations tooling, or subscription commerce products, bonus points if you've been deep in subscriptions
- Bachelor's degree or equivalent practical experience
- 2\+ years building tools operators use daily, admin panels, workflow automation, ops dashboards and an understanding of what breaks when a CS agent hits an edge case
- A track record of shipping working prototypes with tools like Claude Code, Cursor, v0, or n8n; when you propose an idea, you bring a demo, not a spec
- Comfort making build\-vs\-buy calls across the modern AI stack, foundation model APIs vs. fine\-tuning, off\-the\-shelf agent frameworks vs. custom orchestration, vendor tools vs. engineering time
- A view of systems as malleable rather than fixed, with a willingness to move without waiting for permission when something is clearly broken
- Genuine enthusiasm for new AI tools and patterns, and a drive to find where AI can replace slow, manual workflows with something faster and smarter
- The ability to pull insights from data and explain them clearly, experience with SQL, Tableau, or Mixpanel is a plus
- Trust in data balanced with good instincts and an understanding of user psychology
- A focus on impact over titles, scrappy when needed, and comfortable getting things done without waiting for permission
- Experience shipping in fast, cross\-functional teams where priorities shift, while staying grounded and keeping things on track
- Strong collaborative energy, people enjoy working with you, and you know that a healthy team gets better results
- Demonstrated familiarity and working fluency with modern AI tools such as Gemini, ChatGPT, Claude, and similar platforms.
- Ability to leverage AI tools to accelerate market research, assortment analysis, vendor communication drafting, and business insights.
- Experience using AI to synthesize data, generate summaries, and streamline workflow execution.
What You’ll Get! The Benefits \& Perks of FabFitFun* Unlimited PTO
- Generous parental leave
- Company paid holidays (8\)
- Medical, Dental \& Vision
- 401K \& 401K Match
- Pet insurance available for any FFF furry friends
- Complimentary annual FFF subscription with seasonal $25 credit for e\-commerce flash sales
- Donation matching program via BrightFunds, where the company matches up to $500 in charitable donations per employee annually
Compensation
This is a full\-time position with an expected base salary range of $190K \- $210K \+ annual bonus eligibility. The compensation package will also include an initial equity grant, in addition to a range of generous medical, dental, vision and other perks \& benefits. Compensation decisions are determined using a variety of job\-related factors such as skill set, geographic location, market demands, experience, and education / certifications. If we extend an offer for employment, we will consider all individual qualifications.
Privacy
Your information will be handled in accordance with our Workforce Member Privacy Notice: https://legal.fabfitfun.com/\#workforce\-member\-privacy\-notice
Salary Context
This $190K-$210K range is above the median for AI Product Manager roles in our dataset (median: $188K across 140 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 3,708 AI roles we're tracking, AI Product Manager positions make up 5% of the market. At FABFITFUN INC, 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 $216,175 based on 270 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($200K) sits 7% below the category median. Disclosed range: $190K to $210K.
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.
FABFITFUN INC AI Hiring
FABFITFUN INC has 1 open AI role right now. They're hiring across AI Product Manager. Based in Beverly Hills, CA, US. Compensation range: $210K - $210K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 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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