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About This Role
Overview
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We are looking for a highly motivated and experienced Staff Product Manager to join our team and build the future of our ecosystem. In this role, you will lead the strategy and execution for critical product pillars, shifting from manual workflows to "done\-for\-you," agentic AI experiences that customers can trust.
You will own high\-complexity product areas, driving requirements for platform capabilities that operate at massive scale. You will be a systems thinker, moving beyond single features to build reusable, durable systems that serve many teams across the Intuit ecosystem. Whether focused on conversational AI interfaces, automated workflows, or high\-scale platform services, you will partner deeply with engineering and design to deliver step\-change customer benefits.
Responsibilities
Own the Vision and Strategy
- Define the strategic roadmap for your domain, aligning product investments with business goals to drive growth and customer prosperity.
- Lead the shift toward 'Autopilot' and agentic experiences, building systems that behave like high\-performing admins while ensuring customers remain in total control.
Drive AI\-Assisted Excellence
- Leverage AI to develop 'invisible compliance,' catch anomalies, and navigate UX challenges such as latency and hallucinations
- Build AI\-native workflows with a drive towards conversational AI interfaces
- Rapidly iterate on UI designs, perform data analysis, and accelerate cross\-functional delivery
End\-to\-End Experience Ownership
- Design high\-quality, end\-to\-end product experiences across multiple products and domains, maintaining a high bar for product excellence and ease of use
- Own the delivery of foundational experiences that define how customers interact with our agentic ecosystem through natural language conversations
- Partner with engineers and designers to experiment and launch experiences that serve small and mid\-market businesses with emerging AI technologies
Cross\-Functional Collaboration \& Influence
- Partner closely with Engineering, Data Science, Design, and Legal to facilitate trade\-offs between customer experience and technical feasibility
- Consult with teams across the organization to elevate the customer experience, managing dependencies and building shared understanding through data\-backed storytelling
- Scale your work to teams across the ecosystem, building reusable systems that serve many teams simultaneously
Data\-Driven Decision Making
- Take a data\-driven approach to defining success metrics and prioritizing requirements, driving the business toward provable trust
- Gather insights from customers, identify and test opportunities, and synthesize results to continuously refine product approaches
- Create strategies and roadmaps based on data to inform product and platform investments needed to take our products to the next level
Qualifications
- 5\+ years of experience in product management, ideally within a world\-class environment known for high complexity and technical excellence
- Proven track record of delivering complex, user\-facing features from concept to launch, with specific expertise in building AI\-native workflows leveraging generative AI
- Deep comfort with ML/LLM\-powered automation, complex data pipelines, and evaluation loops; ability to engage meaningfully in architecture and design discussions
- Strong understanding of conversational AI, including LLM\-based interfaces, prompt engineering, and the unique UX challenges of generative AI products
- Experience partnering closely with product development and design teams to facilitate trade\-offs between customer experience and technical feasibility
- Mastery of innovation frameworks such as Customer\-Driven Innovation (CDI) and Design for Delight (D4D) to solve important, unsolved customer problems
- Ability to turn customer pain into a durable execution plan, moving fast in ambiguity while building 'provably trustable' systems
- Demonstrated history of taking on increasing responsibility, exhibiting clarity of thinking, and end\-to\-end ownership to deliver impactful results
- Ability to think beyond a single feature to build reusable systems that serve many teams — a true systems thinker
- Bachelor's degree in a technical field (Computer Science, Electrical Engineering, Statistics, Math, or a related discipline) is required to partner effectively with engineering
- Advanced degree or MBA is a plus
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Intuit provides a competitive compensation package with a strong pay for performance rewards approach. This position will 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:
Mountain View $188,500 \- $255,000
New York $186,500\- $252,500
Salary Context
This $188K-$255K range is above the 75th percentile 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 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 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. Disclosed range: $188K to $255K.
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.
Intuit AI Hiring
Intuit has 10 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer, AI Product Manager. Positions span San Diego, CA, US, Mountain View, CA, US, New York, NY, US. Compensation range: $251K - $284K.
Location Context
AI roles in New York pay a median of $220,000 across 1,045 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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