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Overview
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Field service businesses — HVAC, plumbing, electrical, pest control, landscaping — are one of the largest and most underserved segments in the mid\-market. They generate real revenue, employ real teams, and carry financial complexity that most software wasn't built to handle. The infrastructure most of them run on was built for businesses nothing like theirs.
Intuit's strategy is to become the financial operating system for this segment — turning what happens in the field into financial intelligence, automating the workflows that drain owner time, and surfacing the capital products that keep businesses growing.
Field service businesses at this scale carry two distinct financial burdens: job\-based cost accounting — loaded labor, materials, subcontracted work — and contract\-based revenue management — service agreements, retainage, multi\-site billing. Most systems can handle one. Few can handle both. That is the gap Intuit is building to fill.
As the Principal Product Manager for AI\-Native Field Services, you will own the strategy to close it.
Responsibilities
Define Intuit's end\-to\-end field services strategy for the mid\-market — the build, buy, and partner decisions, the roadmap from Residential Financial Foundation through Commercial Contract Depth, and the sequencing bets that determine where we play and how we win.
Make Intuit the financial truth layer behind the operational tools field service businesses already use — jobs as projects, field costs in real time, service agreements as contracts AI agents can reason over.
Build the AI\-powered job P\&L engine — loaded labor rates, technician utilization, and per\-job margin visibility that connects field execution to business outcomes.
Ship the agentic billing roadmap — auto\-generated invoices from service agreement terms, deferred revenue recognition, renewal risk surfacing, and multi\-site commercial billing.
Deliver the AI\-powered cash flow engine with 30/60/90\-day forecasts and agentic AP automation that classifies field expenses, routes approvals, and triggers payment.
Drive adoption of instant customer financing, same\-day technician pay, and proactive working capital — surfaced by agents at the moments that matter.
Qualifications
10\+ years in product management with deep expertise in field service management, workforce management, or mid\-market SaaS serving trades industries — at a field service platform or a financial and ERP platform serving the trades.
Bachelor's degree in Business, Engineering, Computer Science, or a related field, or equivalent practical experience; advanced degree a plus.
Firsthand knowledge of field service operations and trades finance — loaded labor rates, technician utilization, service agreement deferred revenue, commercial AR cycles. Earned in the field, not learned in a spec.
Full job lifecycle command across both residential and commercial models — quoting, dispatch, work order execution, job costing, invoice collection, recurring contract management, and parts and inventory.
Proven ability to ship LLM or agent\-powered products in production — writing agentic specs with objectives, tool definitions, confidence thresholds, and escalation logic, and knowing the difference between automating a workflow and building a system that earns trust incrementally.
Track record influencing across engineering, design, sales, and finance — making sequencing decisions that hold up under pressure and seeing market shifts before they show up on a win/loss report.
Experience architecting bi\-directional integrations as a strategic data foundation and evaluating build, buy, and partner tradeoffs with rigor.
Track record building technician\-facing mobile products where data capture accuracy in the field determines whether financial reporting is real.
Intuit provides a competitive compensation package with a strong pay for performance rewards approach. This position may be eligible for a cash bonus, equity rewards and benefits, in accordance with our applicable plans and programs (see more about our compensation and benefits at Intuit®: Careers \| Benefits). Pay offered is based on factors such as job\-related knowledge, skills, experience, and work location. To drive ongoing fair pay for employees, Intuit conducts regular comparisons across categories of ethnicity and gender. The expected base pay range for this position is:
Bay Area California $ 243,000\- 328,500
Southern California $ 230,000\- 311,000
Salary Context
This $230K-$328K range is above the 75th percentile for AI Product Manager roles in our dataset (median: $189K across 161 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 3,823 AI roles we're tracking, AI Product Manager positions make up 5% of the market. At Intuit, 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 $213,800 based on 583 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($279K) sits 31% above the category median. Disclosed range: $230K to $328K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Intuit AI Hiring
Intuit has 13 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager, Data Scientist, AI Software Engineer. Positions span San Francisco, CA, US, Mountain View, CA, US, San Diego, CA, US. Compensation range: $190K - $357K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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