Market Insights from 592 AI Product Management Jobs

Key Findings

• Most roles are mid-level: 70% of AI product management positions target professionals with 6+ years of experience
• Median salary is $200,500: The middle 80% of roles pay between $129K and $270K annually
• Certifications rarely appear: Just 3% of postings request credentials, with CSPO, PSPO and SAFe POPM leading
• Technology firms dominate hiring: Technology (55%), Financial Services (17%) and IT Services (8%) lead postings
• California leads the market: 31% of U.S. roles are posted in California, followed by New York (19%) and Washington (10%)
• Degrees increasingly required: Only 35% of junior roles require a degree, but that climbs to 61% at senior levels

The Role of an AI Product Management Professional

These patterns align with what we see across AI recruitment, where organizations balance product strategy with technical execution.

We categorized each role by seniority and found the market heavily favors mid-level professionals—they account for more than two-thirds of all postings.

We then extracted experience requirements (89% of roles mentioned a specific number) and calculated the average minimum at each level of seniority. Finally, we analyzed job titles to identify the most common naming conventions at each level.

• Junior (11% of roles)
  • Minimum experience: 2 years
  • Common titles: Product Manager AI Powered Messaging, Product Owner Clinical AI, Product Designer AI
• Mid-Level (70% of roles)
  • Minimum experience: 6 years
  • Common titles: Senior Product Manager AI, AI Product Owner, Product Marketing Manager AI Solutions
• Senior (18% of roles)
  • Minimum experience: 10 years
  • Common titles: Sr. Director Product Management AI/ML Platform Intelligence, Director Product Management AI-led Infrastructure, Principal Product Manager Unity AI

Most AI product management jobs are mid-level

What Do AI Product Management Jobs Involve?

So what is an AI product manager actually responsible for day-to-day? We analyzed the language across all 592 job posts to extract the core responsibilities at each level. What emerged is a clear progression of expectations from execution to strategy:

Junior-Level Roles:

• Execute product delivery by managing backlogs and translating requirements into stories
• Design and build AI-powered features from prototyping to full-stack agentic systems
• Bridge technical and business teams through performance analysis and compliance oversight

Mid-Level Roles:

• Own end-to-end product strategy and roadmap for AI platforms or capabilities
• Drive cross-functional execution to deliver customer-facing AI products and adoption strategies
• Transform ambiguous business problems into working AI products through prototyping and operationalization

Senior-Level Roles:

• Define organizational AI vision and multi-year platform strategy for mission-critical applications
• Establish enterprise AI capabilities and governance frameworks enabling safe AI adoption
• Shape market positioning and partnership strategy while managing executive stakeholder relationships

Key takeaway: Junior product managers execute delivery, mid-level leaders own strategy and roadmaps, senior executives define organizational vision. Each step up means more influence over how AI transforms the enterprise and less hands-on execution.

Who’s Hiring for AI Product Management?

Technology companies lead with 55% of AI product management postings—more than half the market. This concentration reflects the discipline’s technical foundations and the rapid AI adoption within tech firms building internal systems and customer-facing products.

Financial Services follows at 17%, with IT Services rounding out the top three at 8%. Professional Services and Healthcare each capture 5%, completing the top five.

The technology sector’s dominance makes sense given that AI product managers need both strategic product thinking and deep technical fluency—capabilities that tech companies value and can effectively deploy.

Technology companies dominate AI product management hiring

Large companies with 10,001+ employees account for 35% of postings, which is slightly above the broader workforce distribution across the U.S. economy. Organizations with 1,001-5,000 employees add another 19%, meaning roughly half of AI product management roles are at companies with over 1,000 employees.

That said, meaningful demand exists across company sizes. Mid-sized companies (201-500 employees) capture 12% of the market, while smaller organizations (under 200 employees) post 33% of roles combined. This broader distribution suggests both startups and established firms are building AI product capabilities.

Large companies account for just over one-third of AI product management hiring

Where Are AI Product Management Jobs Located?

California and New York account for half of AI product management jobs

California dominates the market with 31% of all AI product management postings—nearly one in three roles. New York follows at 19%, Washington at 10%, Texas at 9%, and Massachusetts rounds out the top five at 7%.

Together, California and New York account for half of all opportunities, reflecting the concentration of tech companies and financial services firms in these states. The geographic spread thins quickly beyond the top markets.

Remote roles account for just 7% of postings despite the strategic nature of the work. This suggests most organizations prefer AI product managers to work on-site or hybrid where they can collaborate directly with engineering teams and business stakeholders.

31% of AI product management jobs are in California alone

Key takeaway: If you’re looking for an AI product management role, California offers three times as many opportunities as any other state. New York and Washington provide the next tier, but expect significantly fewer options—and limited remote opportunities—outside these major tech hubs.

Requirements for AI Product Management Jobs

We analyzed the minimum requirements of each job post and found that most AI product management jobs (52%) require some form of degree. The pattern tightens considerably as you move up the career ladder.

For junior roles, just 35% require a degree (33% bachelor’s, 2% advanced degrees). The remaining 65% don’t specify formal education requirements.

Mid-level positions tighten up: 52% require a degree (47% bachelor’s, 5% advanced degrees).

Senior roles have the strictest requirements: 61% require a degree (56% bachelor’s, 5% advanced degrees).

Degree fields of study that are typically requested of AI product managers include:

• Computer Science (42%)
• Engineering (15%)
• Mathematics (14%)
• Statistics (11%)
• Economics (11%)
• Analytics (10%)
• Software Engineering (8%)
• Information Systems (8%)
• Business (7%)
• Operations Research (7%)

Degree requirements increase as your AI product management career progresses

Requested Qualifications in AI Product Management Job Posts

AI product managers must excel at product strategy, cross-functional collaboration and stakeholder management. Product management and cross-functional collaboration each appeared in 32% of listings, with product strategy following at 29%.

These skills reflect the role’s core function—orchestrating technical teams, business leaders and users simultaneously to deliver AI products that solve real problems.

Data analysis (27%) and communication (25%) round out the top five, emphasizing the need to translate complex AI capabilities into business outcomes through data-driven insights and clear messaging.

Emerging technical skills matter equally. Prompt engineering (9%) and generative AI (8%) increasingly separate AI PM roles from traditional product management.

Just 3% of postings request specific certifications, but when they do, these credentials lead:

• Certified Scrum Product Owner (CSPO)
• Professional Scrum Product Owner (PSPO)
• SAFe Product Owner/Product Manager (POPM)
• Certified Information Systems Security Professional (CISSP)
• Microsoft Azure AI-102 Certification
• Certified Information Privacy Technologist (CIPT)
• Solution Architect Associate (AWS or GCP)
• Chartered Financial Analyst (CFA)

Key takeaway: Strategic product capabilities matter most, but technical fluency with AI systems increasingly differentiates strong candidates. Change management skills like stakeholder alignment separate successful AI product managers from those who only understand the technology.

What do AI Product Management Jobs Pay?

Just over three-fifths (61%) of the AI product management roles we analyzed included an advertised salary.

There was significant breadth in the ranges employers posted, so we normalized the data by selecting the midpoint for our analysis. From our experience, this is generally a much more indicative number for an employer’s target offer—especially in the current market where initial ranges often run wide.

Across the entire dataset of salaries, we found the median salary for AI product management positions to be $200,500. The middle 80% of salaries (10th to 90th percentile) ranged from $129,000 to $270,000.

Most AI product management salaries fall within $129k to $270k

Breaking AI product management salaries down by seniority reveals dramatic progression. Junior roles start at a median $110,000, with mid-level positions jumping 78% to $195,000. The leap to senior adds another 24%—reaching $244,000 median.

What’s notable is the overlap between junior and mid-level ranges. Junior roles at the 90th percentile ($221,500) exceed mid-level roles at the 10th percentile ($137,725) by $83,775. This compression reflects the premium placed on AI product expertise even for less experienced practitioners in this emerging field.

Senior roles show clear separation from mid-level, with the 10th percentile senior salary ($187,500) sitting just below the mid-level median. The ranges also tighten as you move up—junior salaries vary by $159k from bottom to top, mid-level by $111k, but senior roles cluster within a $125k band despite commanding higher absolute numbers.

Senior AI product managers are in the top 4% of U.S. earners

Key takeaway: AI product management positions pay exceptionally well. The median senior-level salary of $240k puts these roles in the top 4% of all earners in the United States. Even junior professionals earning the median $109k land in the top 20%.

Final Thoughts

For Candidates: Build hands-on experience with AI systems early—prompt engineering and generative AI capabilities matter as much as product strategy. For mid-level roles, demonstrating you’ve owned 0-1 product creation or scaled existing AI platforms separates candidates. At senior levels, experience defining multi-year AI vision and establishing organizational capabilities matters more than tactical execution. Agile certifications help, but cross-functional leadership and stakeholder management are table stakes.

For Employers: The tight salary clustering around $195,000 for mid-level roles reflects market maturity—fall significantly below that and expect longer time-to-fill. The strongest signal for senior candidates is experience building organizational AI capabilities and managing executive stakeholders, not just shipping individual features. Remote flexibility remains surprisingly limited in this field, suggesting AI product work requires substantial in-person collaboration.

Methodology

We analyzed 592 AI product management job postings collected from LinkedIn, Indeed and Glassdoor between November 2024 and January 2025. The dataset was limited to full-time roles posted in the United States that explicitly mentioned “AI product management,” “AI product manager” or close variations in the job title.

Duplicate postings were removed using job title, company name and location matching. Seniority levels were determined by analyzing job titles alongside minimum experience requirements stated in each posting. When experience ranges were provided, the lower bound was used for consistency.

Salary data was extracted from the 61% of postings that included compensation ranges. We used the midpoint of each range for analysis, as this most closely reflects employer target offers in practice.

Industry classifications were assigned based on company descriptions and verified against LinkedIn company data where available. Geographic analysis was conducted at the state level using the primary job location listed in each posting.