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Overview
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Job overview
Intuit’s AI Transformation Org is changing how 18,000\+ employees do their most important work. We bring AI agents into the everyday workflows of Finance, Legal, Marketing, Customer Success, and People \& Places. The people doing the work spend less time on coordination and more time on the decisions that need their expertise. We need a Principal who can scale this across the company without taking it over.
This role is the engine that gets AI\-redesigned workflows into production and keeps them there. You partner with AI Champions inside each non\-engineering function. You apply approved enterprise blueprints to high\-friction, repeatable workflows. You set measurable baselines before redesign. You make adoption stick after the central team steps back. Your success is measured by what the function can do after you leave, not by what you personally redesigned.
You partner closely with the People \& Places AI Workforce Transformation team, the Enterprise Solution Architect, the Telemetry and Insights Lead, and senior operating leaders across Intuit’s most consequential non\-engineering functions.
Responsibilities
### Responsibilities
- Drive blueprint\-led redesign with AI Champions across non\-engineering functions. Apply approved blueprint patterns to the workflows we prioritize. Coach Champions through configuration, integration, evaluation, and rollout in their real work, not in training settings.
- Hold the line on blueprint standards. Every redesign uses approved workflow logic, agent configuration standards, integration patterns, governance guardrails, and measurement contracts. Push back on bespoke designs that fragment the enterprise architecture.
- Design redesigns to stick. Set baselines before launch. Instrument the pre\-state with the Telemetry and Insights Lead. Align role expectations and habits with HR Craft Leaders. Validate the post\-state. Treat behavioral adoption as a design constraint from day one, not a retrofit.
- Drive blueprint adoption, not workflow possession. The redesigned workflow is the default execution model in production. The function owns it after you step back. The hand\-off is the deliverable, not the redesign itself.
- Surface friction back to platform and architecture. Identify where blueprint patterns break down, where the platform is missing capability, and where governance creates drag. Feed signal to the Enterprise Solution Architect and the platform engineering team. The platform should compound with every redesign.
- Connect workflow redesign to workforce evolution. Every redesign changes what the people doing the work need to know, do, and decide. Partner with HR Craft Leaders and the People \& Places AI Workforce Transformation team. Role expectations, AI proficiency, hiring criteria, and learning pathways evolve with the workflow, not after it.
- Maintain the redesign portfolio view. Make redesigns in flight, baselines committed, value captured, and risk visible at any moment. Keep prioritization honest. Resist pressure to take on work that does not meet the bar.
Qualifications
Required
- 12\+ years of progressive experience designing, building, or operating production AI/ML systems, agentic workflows, or enterprise workflow platforms.
- At least 3 years driving change inside other people’s organizations. This could be embedded with a business function, a forward\-deployed engineering team, or a transformation function.
- Demonstrated track record of taking AI\-powered workflows from prototype into in\-production adoption at enterprise scale.
- Experience influencing and aligning VP\-level and above stakeholders on workflow strategy and investment decisions.
- Bachelor’s degree required. Computer Science, Engineering, or a related technical field preferred.
Preferred
- Master’s degree in Computer Science, Engineering, or a related technical field.
- Background in enterprise HR, Finance, Legal, Marketing, or Customer Success technology. These are the functions where this role will spend most of its time.
- Experience designing or implementing responsible AI frameworks, evaluation pipelines, or AI governance programs.
- Prior experience as a forward\-deployed engineer or AI solutions architect inside a customer’s organization.
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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:
Mountain View $205,500 \- $278,000
San Diego, CA $189,500\- $256,000
Salary Context
This $205K-$278K 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 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. This role's midpoint ($241K) sits 12% above the category median. Disclosed range: $205K to $278K.
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
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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