AI & ML Talent Insights: 4 Key Takeaways From Our 2025 Report

In 2022, OpenAI launched ChatGPT, and the way we use and think about artificial intelligence changed forever. 

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Today, nearly every tech company is incorporating AI into their products and using AI in their daily workflows. According to McKinsey, 72% of organizations have adopted AI in at least one business function.

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But, only a few years after the AI boom, many compensation leaders are still figuring out how to hire, compensate, and retain top-tier AI and ML talent. That’s why we created the 2025 AI & ML Compensation Trends & Practices Report—a comprehensive guide drawing from Pave’s real-time compensation market dataset. 

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Keep reading to get four of our top takeaways, and download the full report for even more.

1. Machine Learning Job Titles Top the Charts

Many organizations use the titles “AI Engineer” and “ML Engineer” interchangeably—and understandably so. These roles often have similar responsibilities, but there are some differences.

• AI Engineers: Typically, AI Engineers build complete artificial intelligence systems, work on natural language processing, and focus on the theoretical aspects of AI architecture.
   
• Machine Learning (ML) Engineers: On the other hand, Machine Learning Engineers develop systems that can learn and improve with data and build organizational algorithms.

Our data shows that ML Engineer is the most common AI/ML job title, with 83% of roles in Pave’s dataset containing “ML” or “Machine Learning.”

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2: AI/ML Attrition is Higher Than Software Engineering

AI/ML Engineers leave their jobs at a faster rate than software engineers and data scientists. According to our data, the annual attrition rate is 22% for AI/ML managers and 28% for AI/ML individual contributors, compared with 17% for Software Engineers at both levels.

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We also found that higher attrition rates exist at public companies than at private ones. One possible hypothesis is that AI/ML Engineers are rolling the dice on private companies in the hopes of receiving a large equity package while the market is hot. 

In the coming months and years, it will be critical for compensation leaders to focus on retaining AI/ML talent. Some strategies to consider include higher base pay and bonuses, flexible vesting schedules, learning opportunities, project-based incentives, and 401(k) matches. 

3. AI/ML Job Offers Have Drastically Increased

The demand for AI/ML Engineers has skyrocketed, leading to an uptick in offer volume and new hires. 

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According to Pave’s Offer Insights data, the number of accepted offers for AI/ML roles has doubled since May 2023. And, since the end of 2022, the number of AI/ML new hires more than doubled compared with software engineering roles. 

We anticipate offer and hiring volume will continue to rise—demand for AI and ML talent isn’t slowing down anytime soon.

4. AI/ML Talent Is Concentrated in Tier 1 US Cities

Among companies in Pave’s dataset, almost 60% of all AI/ML talent in the world is based in the US. The top five US locations for AI/ML employees are:

1. SF Bay Area 
2. NYC Metro
3. Seattle Metro
4. Boston Metro
5. LA Metro

In addition to these US Tier 1 metros and tech hubs topping the list, another interesting data point is that 50% of AI/ML Researchers in Pave’s dataset are located in the Bay Area alone.

Access More In-Depth AI/ML Salary & Equity Trends

These four insights are valuable, but they only scratch the surface. In the report, you’ll also find information about:

• Median base salary for AI/ML Engineers
• Median new hire equity grant at private vs public companies
• Percent of AI/ML offers that include a sign-on bonus
• Equity vesting schedules at private vs public companies
• And much more!

Get ready to build a competitive and data-driven Artificial Intelligence and Machine Learning compensation strategy. Download the 2025 AI & ML Compensation Trends & Practices Report.