Not at Harvey Mudd? How to compete for tech jobs anyway

What the placement data shows

College Transitions recently ran a useful number on LinkedIn data: for every 1,000 early-career graduates of a school, how many hold software engineering jobs in the tech sector? Their January 2026 analysis looked at graduates from 2015 onward with under five years of experience.¹

The top of the list is small. Harvey Mudd College placed about 281 per 1,000. Caltech came in at 251, MIT at 214, Carnegie Mellon at 208, and Stanford at 155. UC Berkeley led public universities at about 113, with UC San Diego close behind at roughly 100.¹

Now compare that to the broad middle. College Transitions puts the rate for less selective private colleges at about 5 per 1,000, a gap of more than 50 to 1 between the top school and a typical private campus.¹

If you are already enrolled somewhere that is not on that list, the data can feel like a verdict. Read it instead as a map of what the top schools do differently. Much of that you can copy.

The entry-level squeeze makes the gap matter more

This data lands in a hard market for new grads. The Federal Reserve Bank of New York reports that the unemployment rate for recent college graduates "stayed elevated" at about 5.6 percent in the second quarter of 2026.²

AI looks like one factor for the youngest workers. A Stanford Digital Economy Lab paper by Erik Brynjolfsson, Bharat Chandar, and Ruyu Chen, revised August 12, 2026 with payroll data through June 2026, finds that employment for workers aged 22 to 25 in AI-exposed occupations is 19 percent below where it would be had it kept pace with less-exposed peers.³ Experienced workers show no comparable gap. The authors say the drop comes mostly from fewer hires rather than more layoffs, and it is concentrated in work where AI substitutes for people instead of helping them.³

Put those together. Entry-level hiring in AI-exposed work has slowed, and employers filling the openings that remain lean on signals they trust. A Caltech or Harvey Mudd degree is one of those signals. If your school's name does not do that work for you, your evidence has to.

What the top placers have in common

College Transitions points to Harvey Mudd's Clinic Program as one explanation, though the analysis does not test that claim directly.¹ Clinic is worth understanding anyway. Juniors and seniors work in teams of four or five on a technical problem for an outside company or organization. The college counts 641 sponsors and 2,070 projects over 64 years.⁴

When a Mudd grad applies, a hiring manager sees someone who has already shipped work for a real client, on a team, with a deadline. That removes the risk a company worries about most before it pays an entry-level salary.

You can build a version of that at almost any school.

Two things to do this term

1. Find one real client project. Ask your career center or a professor with industry ties whether a local nonprofit or campus office needs a technical problem solved. A small one is fine: an internal tool or a data cleanup. What matters is that someone outside your classroom uses the result. Write down the problem, what you built, and what changed for them, with a number if you can get one.

2. Turn finished coursework into proof. Most students list a class project as a line on a resume and stop there. Take your strongest project and publish it with a short write-up covering what it does, the hardest decision you made, and what you would change. Link it from your resume and your LinkedIn Featured section. A recruiter skimming a stack of applications will click a link far sooner than reread a list of courses. How to turn a class project into a resume bullet walks through the wording.

Check the path as well as the school

The placement data cannot tell you how exposed the specific job you are aiming for is to AI. The Stanford findings suggest that young-worker hiring has dropped in roles where AI mainly does the task, while roles where AI assists the worker have stayed flat or grown.³ Two jobs with the same title can sit on different sides of that line depending on the tasks involved.

Before you commit a year of projects to one path, look at the tasks that make up that job and ask which ones a model can already do. The guide on why no major is AI-proof, and how to evaluate one walks through that check. Cairn Careers is built for that check: it scores each path a student is weighing for AI exposure, task by task, with every source linked, as the methodology explains.