Introduction
So how do you actually spot excellent non-traditional candidates? And why do we know more about this than the average practitioner?
As you saw above, for six years we ran the world's largest blind hiring experiment for engineers. In that time we placed thousands of engineers at FAANG-level companies, adjacent firms, and leading startups. Among our users who received offers from top-tier companies, 46% (nearly half!) had neither a prestigious school nor a top company on their resume. In a conventional (non-blind) hiring process, those candidates would never even have been invited to interview.
What's more, many of these non-traditional candidates had previously been rejected by the very company that later hired them through us. Many of our clients were surprised to learn, after a candidate was "unmasked," that the person already existed in their ATS and had been turned down at the resume screen.
Because we had all that candidate data, we could review our list of high-performing non-traditional hires and find common patterns in their resumes. Here are the main trends and what to look for.
Teaching assistants (TAs), tutors, and graders
A candidate from a state university who worked as a teaching assistant was trusted to explain hard material and evaluate others' work. That says far more about competence than a brand-name diploma.
Graduates of Bradfield School of Computer Science
This sounds oddly specific, but look for graduates of Bradfield School of Computer Science. Bradfield is unfortunately on "indefinite pause," but for years it was a small, rigorous program built to fill gaps left by traditional CS degrees and bootcamps. Students went deep on operating systems, compilers, and system design — the hard foundations great engineers need. If you see Bradfield on a resume, you're looking at someone who chose to invest serious time in fundamentals.
"Distance traveled" and breaking the mold
Freada Kapor Klein of Kapor Capital coined "distance traveled" more than two decades ago. It describes what someone has achieved relative to where they started. For example, Kapor Klein recommends that college admissions weigh not only how many AP exams a student passed, but that number relative to how many AP exams their high school offered.
Kapor Capital uses "distance traveled" as one criterion for which entrepreneurs to back. The idea transfers easily to hiring. Candidates who taught themselves to code while holding another job, switched careers, or repeatedly outpaced expectations (faster promotions than peers, ambitious side projects, contributions to hard open-source problems) tend to keep doing so. That mix of grit and self-direction is hard to teach — and worth betting on.
High-signal bullet points
Look for bullets that describe what the candidate actually built or owned — not a pile of generic phrases.
Weak example
Strong example
About sections that read like a sharp, invested person wrote them
Here are two examples. The first is a faceless word salad. The second gives a clear sense of who this person is and why they stand out.
Faceless version
Compelling version
Fewer than two typos or grammar mistakes
In one past study, typo count was the strongest resume attribute: the strongest candidates consistently had two or fewer typos. Typos and grammar mistakes were far more predictive than, for example, where the candidate worked previously.
Sample profiles of excellent non-traditional candidates
Because showing beats telling alone: profiles of three strong hires. All three now work at FAANG and FAANG+-level companies, but before their first "brand" opportunity their profiles looked different — without prestigious labels to lean on.
And remember: non-traditional candidates are twice as easy to attract, and they stay at companies 15% longer.
We built a model that can do this for you
It's easy to say you should hire non-traditional candidates, but hard to apply the advice when you're staring at a mountain of resumes. Reading every bullet and About section is unrealistic for recruiting teams at scale.
We spent years figuring out which signals matter and what separates "diamonds in the rough" from just rough rock. Now we have a predictive model that helps surface strong candidates regardless of how they look on paper.
Today our model converts first conversations to hires at 12% — several times higher than typical rates through many companies' own funnels.
Want to hire excellent people? Use the IT‑Career‑Gym platform as a source of strong candidates and interview-prep practice.