Top Grad Programs for Careers in AI

The jobs most exposed to AI aren’t disappearing. They’re outperforming the rest of the labor market in both growth and wages, according to a December 2025 Vanguard analysis.

But exposure to AI isn’t the same as vulnerability to it. Employment for workers ages 22 to 25 in roles that are vulnerable to AI, including entry-level software developers and customer service agents—where the technology can replace core tasks—has fallen 16% since ChatGPT was released in 2022, a National Bureau of Economic Research study found.

For roles with high exposure to AI, on the other hand, a graduate degree in AI or machine learning can position you to build the technology rather than compete against it—leading to jobs that continue to command strong pay and growth.

AI and machine learning (ML) engineer salaries now average $152,581, and the Bureau of Labor Statistics projects 20% job growth for computer and information research scientists—a category that includes many AI roles—through 2034. That pace far exceeds the 3% average projected for all occupations.

Where the Top Programs Are

Here’s what distinguishes the top programs: research you can participate in, faculty publishing at the frontier of the field, and major employers who recruit directly from the program.

Carnegie Mellon launched the first dedicated machine learning department in 2006. It offers master’s programs, and its PhD tracks include joint programs in statistics, public policy, and neuroscience—combinations that reflect how AI is spilling into other fields.

MIT’s Electrical Engineering and Computer Science department houses the Artificial Intelligence and Decision-Making unit, covering everything from reinforcement learning to robotics. Meanwhile, Stanford’s AI Lab, founded in 1963, is one of the oldest programs in the field and now offers an online graduate certificate.

Berkeley’s BAIR Lab, Illinois’s Grainger College, and Georgia Tech’s College of Computing have deep benches in computer vision, natural language processing, and machine learning. The University of Washington collaborates directly with the Allen Institute for AI, while the University of Texas at Austin and Cornell University have boosted their efforts in applied AI research.

What To Look For in a Program

The right program launches and accelerates your career, while the wrong one can leave you with student loan debt and no network.

Be skeptical of flashy new AI degrees that lack a track record, and note that rankings matter less than the program’s fit for your interests and career plans. Also look for evidence that graduates got jobs. For instance, check LinkedIn to see if graduates have landed at companies or labs you’d want to work for. Also check whether research opportunities, paid internships, or capstone projects are built into the curriculum.

Another smart move is looking at industry pipelines: Google, Meta (META), and Nvidia Corp. (NVDA) actively recruit from and support the top AI labs. Make sure a program’s curriculum covers actually creating ML and AI systems, not just theory. Avoid programs that neglect the development skills that are important for getting hired. And check whether faculty are publishing in top journals and presenting at conferences, which signals the program is actively shaping the field.