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About This Role
Overview
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Come join the Intuit Development Platform team as a Principal Product Manager. The Intuit Developer Platform team builds foundational capabilities that developers across Intuit use for building and running Intuit’s flagship applications leveraging cloud\-native technologies in a highly scalable, reliable, and secure manner. Ease of use along with scale and reliability are some of the key attributes of the platforms that this organization is responsible for. Intuit is a big believer of open source both as a consumer and as a contributor and actively participates in many open\-source communities, including CNCF, Istio, and GraphQL.
We are on a journey to enable hyper\-scale on both our systems and our development processes and are making fundamental technological shifts that will enable this journey. As a product manager in this group, you will have the opportunity to solve business\-critical problems via leading\-edge products, participate in the open\-source community, and lead innovation within and outside of Intuit.
We are looking for candidates with extensive experience in product management, particularly in building scalable platforms. The ideal candidate will have a strong technical background, excellent customer empathy, and a proven track record of executing strategies and forming strong business partnerships that deliver significant results. As a Principal Product Manager on Intuit’s Developer Platform team, you will focus on enhancing the entire Innerloop software development experience and integrating it with the overall development lifecycle using GenAI technologies. Your role will involve driving the product strategy to enrich the developer experience, address developers' needs, and boost their productivity and innovation. Additionally, you will ensure that our GenAI tools provide personalized support to developers by suggesting code snippets, retrieving documentation, and offering real\-time feedback. This position is perfect for someone passionate about using GenAI to transform the end\-to\-end development process. Responsibilities
- Define and prioritize product features and roadmaps to meet the needs of developers and align with Intuit’s strategic goals.
- Develop and execute a comprehensive product strategy for enhancing the Innerloop software development experience.
- Enrich the developer experience by addressing their needs, boosting productivity, and ensuring personalized support from GenAI tools.
- Form strong business partnerships with key stakeholders to ensure seamless integration of AI\-assisted development tools into existing workflows and tools.
- Oversee knowledge management and discovery of APIs, code, and relevant data to deliver GenAI user experiences that facilitate knowledge and code discovery.
- Enable context\-aware code generation and manage the lifecycle of code generation capabilities to support Intuit’s development processes.
- Define and track key performance metrics to measure the success and impact of GenAI tools on developer productivity and satisfaction, using data\-driven insights to inform product decisions..
- Take a data\-driven approach in prioritizing requirements, creating and communicating plans and roadmaps, defining success metrics to deliver impactful business results.
- Own the product lifecycle from requirements gathering through production and drive building products that deliver great customer experiences
- Demonstrate customer obsession by being a champion for the user experience with deep customer empathy
Qualifications
- 8\+ years designing and delivering world\-class platform products that solve big customer needs, specifically in large scale distributed systems and platforms
- Excellent understanding of SDLC and experience building development facing products and an appreciation for the "developer" persona.
- Has experience defining, building, and delivering products on public clouds – AWS, Azure, and GCP.
- Has a good understanding of defining and building SaaS services, products, and infrastructure at scale.
- Has worked in an Agile development setting and can drive backlog grooming and prioritization.
- Product development and technical background, either as an engineer or in a previous technical role with prior hands\-on development experience or in developing and presentation of technical demos.
- Ability to take end\-to\-end responsibility for the product’s success.
- Ability to work closely with customers and partners listening and taking a customer\-focused approach to defining strategy and setting priorities demonstrating a passion for the customer.
- Excellent collaboration and communication skills \- leading execution across teams, influencing across organizations in small teams, and larger group presentation formats.
- Undergraduate degree in computer science, electrical engineering or a related technical discipline required. Equivalent work experience will be considered. Advanced degree or MBA preferred.
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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 $243,000 \- $328,500
San Diego, CA $230,000\- $311,000
Salary Context
This $243K-$328K range is above the 75th percentile for AI Product Manager roles in our dataset (median: $185K across 167 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 4,317 AI roles we're tracking, AI Product Manager positions make up 4% 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 $217,100 based on 471 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($285K) sits 32% above the category median. Disclosed range: $243K to $328K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Intuit AI Hiring
Intuit has 12 open AI roles right now. They're hiring across AI/ML Engineer, Research Scientist, Data Scientist, AI Product Manager. Positions span New York, NY, US, Mountain View, CA, US, San Diego, CA, US. Compensation range: $190K - $328K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 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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