Important points
- Roles related to building AI systems are thriving, with average salaries exceeding $150,000.
- Top graduate programs at Carnegie Mellon, MIT, and Stanford will position you to build technology, rather than compete with it.
- When evaluating programs, focus on research and career opportunities, not just rankings.
The jobs most exposed to AI are not going away. They are outperforming other labor markets in both growth and wages, according to Vanguard’s December 2025 analysis.
But exposure to AI is not the same as vulnerability to AI. A study by the National Bureau of Economic Research found that employment among workers aged 22 to 25 in jobs where AI could replace core jobs, such as entry-level software developers and customer service agents, has declined by 16% since 2022, when ChatGPT was released.
On the other hand, in jobs that have a lot of exposure to AI, a graduate degree in AI or machine learning can position you to build the technology rather than compete with it, leading to jobs with higher pay and growth potential.
AI and machine learning (ML) engineers currently earn an average salary of $152,581, and the Bureau of Labor Statistics predicts that employment for computer and information research scientists, with many AI roles, will increase by 20% by 2034. This pace is much higher than the average expected 3% for all occupations.
Top program locations
Features of the top programs include: Research you can participate in, faculty at the forefront of the field publishing, and major employers hiring directly from the program.
Carnegie Mellon University launched its first machine learning department in 2006. We offer master’s and doctoral programs. The tracks include collaborative programs in statistics, public policy, and neuroscience, and their combination reflects how AI is permeating other fields.
MIT’s Department of Electrical Engineering and Computer Science has an Artificial Intelligence and Decision Making Unit that covers everything from reinforcement learning to robotics. Meanwhile, Stanford University’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’ Grainger College, and the Georgia Institute of Technology College of Computing have deep benches in computer vision, natural language processing, and machine learning. The University of Washington is collaborating directly with the Allen Institute for AI, and the University of Texas at Austin and Cornell University are increasing their efforts in applied AI research.
tips
Online options are expanding. Georgia Tech, University of Texas at Austin, and University of Illinois at Urbana-Champaign all offer highly regarded online AI master’s programs, often at lower costs than comparable on-campus programs.
What to look for in a program
Starting the right program can accelerate your career, but choosing the wrong program can leave you saddled with student loan debt and losing your network.
Be skeptical of flashy new AI degrees without a proven track record, and remember that rankings are less important than whether the program fits your interests and career plans. Also look for evidence that graduates have obtained employment. For example, check LinkedIn to see if alumni have jobs at the companies or labs you’d like to work for. Also, see if there are research opportunities, paid internships, or capstone projects built into the curriculum.
Another smart move is to focus on the industry pipeline. Google, Meta (META), and Nvidia Corp. (NVDA) are actively hiring and supporting talent from top AI labs. Make sure your program’s curriculum covers the actual creation of ML and AI systems, not just theory. Avoid programs that neglect development skills that are important for recruitment. Also, check to see if faculty members have published papers in top journals or presented at conferences. This shows that the program is actively shaping the field.
