How to spot great non-traditional candidates (when you can't rely on brands)

This is the fourth post of four in our hiring series.

Part 1: For six years, we ran the largest blind eng hiring experiment of all time. Here’s what happened.

Part 2: We built an AI model for eng hiring that outperforms recruiters and LLMs

Part 3: More filters, same broken process: how AI sourcing tools are making hiring worse

If you've been in eng hiring for any length of time, you've probably noticed that the candidates who look best on paper don't always turn out to be the best engineers, and some of the strongest people you've ever worked with often took wildly unconventional paths to get there.

Despite that, most hiring pipelines are still built to filter for pedigree and brands. Worse, we're now automating that bias at scale with a new generation of AI sourcing and filtering tools. These tools can get increasingly granular: not just where someone worked and went to school, but which VCs have backed their past companies, whether they played sports in college, and so on. These proxies don’t actually mean the candidates are any better, and they certainly don’t mean those candidates are going to want to work for you. I wrote about how AI-driven searches are making it harder to hire and the resulting “technical recruiting death spiral” in my last post.

This post is about something else. There's a massive talent arbitrage opportunity for recruiting teams who are willing to look beyond pedigree and brands. As everyone's AI-powered funnels converge on the same narrow set of profiles, a growing long tail of talented engineers — people who are smart, who can get things done, who will be easier to close and stay longer — are being systematically overlooked. Anyone who figures out how to identify these people is ironically going to win the talent war.¹

Before I talk about how to spot great non-traditional talent, I’d like to convince you that the usual signals we look for aren’t that predictive and that recruiters (and hiring managers) aren’t particularly good at getting signals from resumes. If you, like many people in our industry, believe that candidate pedigree is the be-all and end-all of hiring, I don’t know if I’m going to be able to convince you otherwise, but I’ll sure as hell try.

Pedigree is not a great predictor of performance

Here’s all the data we have, drawn from tens of thousands of interview outcomes. Conclusively, pedigree (that is, having worked at top companies and having attended top schools) does not reliably predict performance.

Our best evidence is that for six years, we hired thousands of people for FAANGs, FAANG-adjacent and top-tier startups. 46% of the candidates who got offers didn’t have top-tier brands on their resumes. And those candidates were 2X easier to close and spent 15% longer at the companies who hired them.

And some more evidence:

• Typos on resumes are a better predictor of performance than where someone worked
• Where someone went to school barely matters for technical interview outcomes
• Whether someone took MOOC courses is a better predictor of interview performance than where they worked or where they went to school

Probably because pedigree is not a useful signal, neither recruiters nor hiring managers can identify what a good candidate looks like.

We ran two studies, ten years apart, where we showed anonymized resumes to recruiters and hiring managers and asked them to identify the strong candidates. Not only did they do poorly at this task (about as well as a coin flip), but everyone disagreed on what a good candidate looked like.

• First study (recruiters, engineers and hiring managers)
• Second study (just recruiters but with 3X the data points)

How to spot great non-traditional candidates

OK, so how do we actually spot great non-traditional candidates? And why do we know more about how to do it than the average bear?

As you saw above, we ran the world’s largest blind eng hiring experiment for six years. In that time, we placed thousands of engineers at FAANG and FAANG-adjacent companies and top-tier startups. Of our users who got offers from top-tier companies, 46% (almost half!) didn’t have either a top school or a top company on their resume. In a normal (not blind) hiring process, these candidates wouldn’t even have gotten an interview.

In fact, many of these non-traditional candidates had been rejected by the very same company where they later got an offer through us. Many of our customers were surprised to see that, after the candidate unmasked, that the candidate had already existed in their ATS and had previously been rejected at the resume stage.

Because we had all this candidate data, we were able to look through our roster of non-traditional top performers and see what their resumes had in common. Here are the trends and what you should look for.

1. TAs, tutors, and graders in upper-level CS classes at state schools and second-tier schools. A candidate from a state school who served as a teaching assistant was trusted to explain complex material and evaluate others’ work. That’s far more predictive of competence than a brand name on a diploma.

2. On the other hand, having been a coding bootcamp instructor is a BAD signal. It often correlates with people who couldn’t find traction as engineers and pivoted to teaching too early.

3. This is oddly specific, but look for graduates from the Bradfield School of Computer Science. Bradfield is sadly on an “indefinite hiatus”, but for years, it was a small, but very rigorous program built to fill the gaps left by traditional CS degrees and bootcamps. Students went deep on operating systems, compilers, and systems design, i.e., the hard stuff that great engineers need to know. If you see Bradfield on a resume, you’re looking at someone who chose to invest serious time in mastering fundamentals.

4. Distance traveled and pattern breaking. Freada Kapor Klein from Kapor Capital coined the term “distance traveled” more than two decades ago. It refers to what someone accomplished, in the context of where they started. For instance, Kapor Klein recommends that, in their admissions processes, universities should consider not just the number of AP tests a candidate has passed but the number of AP tests divided by the total number offered at their high school. For example, if an applicant took 5 AP tests and their school offered 27, that paints a very different picture from another applicant who also took 5 AP tests when that’s the total number offered at their school. Kapor Capital uses distance traveled as one of their metrics for determining which entrepreneurs to fund. One can easily apply this concept to hiring as well. Candidates who taught themselves to code while working another job, switched careers, or repeatedly punched above their weight — promotions faster than peers, shipped ambitious side projects, contributed to hard open-source problems — tend to keep doing it. That combination of persistence and self-direction is hard to teach and worth betting on.

5. High-signal bullet points that read like they were written by a smart person who cares. Look for bullets that describe what they actually built or owned, not word salad.

   ❌ Participated in agile ceremonies

   ❌ Worked in cross-functional teams and communicated effectively on business goals

   ✅ Built a caching layer for the internal analytics dashboard used by sales and support, so queries stopped hitting production and page loads dropped from minutes to seconds.

6. Similarly, “About” sections that read like they were written by a smart person who cares. Here are two examples. The first is unmemorable word salad. In the second, you can get a good idea of who the person is and why they’re impressive.

   ❌ Experienced software professional 10+ years of experience spanning development, engineering, team leadership, requirements analysis, and delivering solutions across the full SDLC. Skilled at coordinating and implementing practices that drive meaningful improvements in software development workflows and help organizations meet their goals.

   ✅ I work on the messaging system for one of the most popular social media platforms in the world. I have over 10 years of experience in building efficient, reliable, and maintainable systems for various domains, including stock market prediction, military simulation, and computer vision. I specialize in using modern C++ (C++11 and up) and Python to develop and deploy systems. I have a strong background in algorithms and data structures, image processing, pattern recognition, and mathematics.

7. Fewer than two typos or grammatical errors. In some past research, I learned that the number of typos is the most important attribute of a resume and that the resumes of the strongest candidates consistently had two or fewer typos. The number of typos/grammatical errors was way more predictive than where candidates had previously worked, for instance.

Example profiles of great non-traditional candidates

Because I’d rather show than just tell, here are the profiles of three of our top-performing hires. All three are now at FAANG and FAANG+ companies, but I’ll show you what their profiles looked like before they got their first break.

Candidate 1

Candidate 2

This candidate was kind enough to let us unmask him, so you can visit his LinkedIn and take a look for yourself!

Candidate 3

This candidate was also kind enough to let us unmask him, so you can visit his LinkedIn and take a look for yourself!

And remember, non-traditional candidates are 2X easier to close and stay at companies 15% longer.

We built a model that can do this for you

It's easy to say that you should hire non-traditional candidates, and I hope the tips above are helpful to you, but implementing them is hard when you’re dealing with a mountain of resumes. Reading every bullet and “About” section isn’t realistic for recruiting teams working at scale.

Fortunately, we spent years figuring out what signals to look for and what separates diamonds in the rough from, well, the rough. We now have a predictive model that outperforms both human recruiters and LLMs and can reliably identify strong candidates, regardless of how they look on paper, just from a LinkedIn profile. Not only can it spot diamonds in the rough, but it can also identify candidates who look good but aren't actually good.

We can help you find the diamonds in your own candidate pool. So far, our model has a 12% first conversation-to-hire rate. That’s 5X what most top-tier companies are seeing through their own efforts.

Want to hire great people? You can use us as a source of excellent candidates or you can use our predictive model to help you surface diamonds in the rough (just like the ones above) from your own candidate pool. Just fill in this form, and we’ll be in touch.

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