"The State of AI Talent: A Compensation & Workforce Report" draws on Pave's real-time compensation dataset of 9,000+ companies, including 80% of the Forbes AI 50, to reveal where AI/ML pay, hiring, and retention are heading — and why the biggest premiums show up in equity, not base salary.
SAN FRANCISCO, Aug. 24, 2026 /PRNewswire/ -- Pave, the AI compensation platform, together with Nua Group, a human resource consulting firm specializing in compensation and HR optimization, today released "The State of AI Talent: A Compensation & Workforce Report," a data-driven look at how companies structure, pay, and retain AI and machine learning talent. The report analyzes employees mapped to AI and ML jobs across Pave's real-time compensation dataset of 9,000+ companies, including 80% of the Forbes AI 50, sourced through automated, persistent connections to HRIS, ATS, and equity management systems.
The findings show a market that has matured enough to split AI Engineering, ML Engineering, and AI Research Scientist into distinct job families — each with its own pay curve, seniority mix, and hiring trajectory.
Key findings include:
- The real compensation premium lies in equity, not base salary. Median base pay is relatively compressed across the three core job families — ML Engineering actually leads at senior levels, with $321K at P6 and $347K at M6. But median new-hire equity for AI Research Scientists reaches $4.09M at P6 and $4.72M at M6, more than double their ML Engineering and AI Engineering counterparts at the same levels.
- Massive equity grants are prevalent for AI Research Scientists at private SF Bay Area companies that have raised $1B–$5B+, 90th percentile new-hire equity packages at the most senior levels reach into the $45M–$60M range.
- AI Engineering is now the fastest-growing job family. Its share of all employees grew from 0.010% in Q4 2023 to 0.185% in Q2 2026, with its share of new hires spiking from 0.11% to 0.35% over the same period — roughly 10x growth.
- AI/ML talent is highly concentrated at scale and on the coasts. Just 12% of companies with 1–99 employees have any AI/ML talent, versus 91% of companies with 3,000+ employees. California alone holds 49.3% of US-based AI/ML professionals, and the US accounts for 57.3% of this talent globally.
- Retaining AI/ML talent has become increasingly challenging. Over the last 12 months, AI/ML turnover for individual contributors ran 21.8%, versus 16.8% for software engineering at the same companies.
- Recent hires in AI/ML are out-earning their incumbent peers. . Employees hired in the past six months into AI Research Scientist roles show median base salary premiums of up to 10% over incumbents at the same level — a gap the Recent Hire Filter, now available in Pave's Market Data Pro, is designed to diagnose.
"AI talent is the fastest-moving hiring market we have ever measured, and it's moving in ways an annual survey simply cannot see," said Matt Schulman, CEO of Pave. "Base salaries look compressed on paper, but the real competition is happening in new-hire equity and at the point of hire, where recent hires are commanding meaningful premiums over incumbents. If you're benchmarking this market against data that's even six months old, you're not looking at the market — you're looking at its history."
The report also introduces a practical framework, developed by Nua Group, for companies to "know their AI lane" before building a pay structure — sorting organizations into AI Innovators, AI Integrators, and AI Implementers, each with a distinct hiring profile and cash-and-equity posture.
"We built this framework because we kept seeing the same pattern across clients," said Ryland Bauer, Total Rewards Advisor at Nua Group. "A company would come to us needing help pricing 'AI Engineering talent,' and what they actually needed looked completely different depending on whether they were building frontier models, embedding AI into a product, or applying it inside an existing business. Once you know which of those three you are, the rest of the compensation decisions get a lot more straightforward."
"The most expensive mistake we see isn't paying too much — it's paying for the wrong role," Schulman continued. "The market has matured to the point where AI Engineering, ML Engineering, and AI Research Scientist are genuinely distinct job families with distinct pay curves. Companies that get that job architecture right, and then revisit their bands more often than they would for any other engineering family, are the ones making offers they can defend to candidates, to finance, and to their boards."
The full report, including level-by-level base salary and new-hire equity benchmarks, hiring velocity trends, turnover analysis, and the Know Your AI Lane framework, is available at: https://explore.pave.com/The-State-of-AI-Talent.html
About Pave
Pave is the AI compensation platform, purpose-built for compensation leaders. Through real-time connections to HRIS, ATS, and equity systems, Pave gives teams a continuously current data foundation for benchmarking, building pay ranges, running merit cycles, and communicating compensation to employees. The Pave Agent, Pave's AI compensation analyst, reasons across that foundation to deliver explainable, advisory recommendations that compensation teams can defend to their CEO, board, and employees. Pave is headquartered in San Francisco. Learn more at pave.com.
About Nua Group
Nua Group stands with the companies where people matter. Nua Group is an independent broad-based human resource consulting firm specializing in compensation, equity, and HR optimization, focused on providing companies with unbiased, holistic, and expert advice. Learn more at NuaHR.com.
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SOURCE Pave
