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Job Description:
Job Description Summary
This Principal Product Manager – AI Solutions role is a hands\-on, individual contributor position that creates and delivers AI\-first product capabilities for Vertex customers by translating ambiguous problems into clear hypotheses, product requirements, and roadmap priorities. The role partners closely with tax domain subject matter experts (SMEs), engineering, UX, go\-to\-market, and customer success to ship production\-ready AI that is trusted and compliant, integrates into workflows, and demonstrates value through evaluation, monitoring, and performance metrics. This role is not a tax SME; it is an innovation and product leadership role that identifies opportunities, prototypes solutions, and scales what works.
Required Qualifications
- Seeking a minimum of six (6\) years of relevant product management experience. Principal\-level success includes owning multi\-quarter roadmaps, aligning cross\-org stakeholders, and leading complex, cross\-functional initiatives from ambiguous problem definition through 0 1 discovery, launch, rapid iteration, and scaled releases with measurable customer and business outcomes.
- 2\+ years of direct AI experience (e.g., system design and/or development).
- Experience to define AI product requirements beyond feature scope, encompassing data strategy, learning signals, feedback loops, and measurable success criteria.
- Strong command of AI product quality and reliability, including how drift, regressions, and performance tradeoffs affect user trust and adoption.
- Excellent product leadership fundamentals, including systems\-level thinking, executive communication, and the ability to lead through influence in complex, matrixed organizations.
- Set and own standards for AI product effectiveness, including success metrics, monitoring, and continuous improvement across quality, reliability, usage, cost, and performance.
- AI fluency (required): able to prototype, evaluate, and operationalize AI\-first capabilities in partnership with engineering and SMEs.
+ Define features and capabilities by creating lightweight prototypes; translate AI capabilities and constraints into clear product requirements.
+ Lead evaluation, tuning, and training in partnership with engineering and SMEs, including defining evaluation criteria and interpreting results.
+ Refine requirements, UX behaviors, acceptance criteria, and release readiness decisions by connecting evolving AI capabilities to customer problems.
+ Apply responsible AI principles (traceability, privacy, security, bias awareness, transparency, auditability) within owned features and capabilities.
+ Maintain working fluency in modern AI/ML concepts (LLM\-based solutions, agents, data pipelines) and use that fluency to experiment directly for faster product discovery and prototyping.
Preferred Qualifications
- Bachelor’s degree in engineering, business, or a related field, or equivalent combination of education, training, and relevant professional experience.
- Experience identifying cross\-product innovation opportunities and driving roadmap alignment through APIs, integrations, and shared platform capabilities.
- Experience mentoring or coaching junior product owners on AI fluency and product development practices.
COMMENTS:
The above statements are intended to describe the general nature and level of work being performed by individuals in this position. Other functions may be assigned, and management retains the right to add or change the duties at any time.
Vertex Values: Together We Win
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We're building a team of people who are passionate about making an impact for our customers and committed to how that impact is achieved. Our values define the behaviors, mindset, and culture that make Vertex a great place to grow and do meaningful work.
Play to Win or We Don't Play — If we choose to do something, we're choosing to do it because we plan to win. That mindset raises our bar on product quality, customer outcomes, and how we show up for one another.
Work As a Team, Putting the Customer At the Core — Our customers are our true north. Whatever your role, ask: how will this help a customer succeed today? We earn trust through outcomes, not promises.
Achieve Excellence With Integrity, Speed, and Agility — The market isn't slowing down. We'll move faster, adapt quickly, and never compromise on doing things the right way — for teammates, customers, and partners.
Innovate Boldly With a Growth Mindset — Progress demands smart risk. We'll try new approaches, learn fast, and keep pushing the boundaries — especially where AI can remove friction and unlock value.
Communicate with Care, Candor and Transparency — Honest, constructive conversations make us better. Let's speak plainly about what's working and what isn't and help each other improve.
Pay Transparency Statement:
US Base Salary Range: $189,600\.00 \- $246,400\.00
Base pay offered to new hires may vary based upon factors including relevant industry and job\-related skills and experience, geographic location, and business needs.\* The range displayed does not encompass the full potential of the role, which allows for further growth and career progression.
In addition, as a part of our total compensation package, this role may be eligible for the Vertex Bonus Plan (VOB), a role\-specific sales commission/bonus, and/or equity grants.
Learn more about Life at Vertex and connect with your recruiter for more details regarding Vertex's compensation and benefit programs.
- *In no case will your pay fall below applicable local minimum wage requirements*.
Salary Context
This $189K-$246K range is above the median 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 Vertex, 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 $217,100 based on 471 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. Disclosed range: $189K to $246K.
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.
Vertex AI Hiring
Vertex has 1 open AI role right now. They're hiring across AI Product Manager. Based in Remote, US. Compensation range: $246K - $246K.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% of all AI roles offer remote work.
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.
Frequently Asked Questions
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