Aline
Lerner

The Data Nobody Wanted to Hear

Here's the thing Aline Lerner found when she dug into thousands of resumes and interview outcomes at her own recruiting firm: typos and grammatical errors predicted candidate quality more reliably than where someone went to school. Not GPA. Not company name-drops. Typos. The resume industry — worth hundreds of millions in coaching, templates, and optimization — rests on a foundation she systematically dismantled with a spreadsheet.

Her blog posts on the subject weren't editorials. They were studies. When she published her findings on why resumes are a low-signal filtering tool, millions of people read it. Forbes wrote about it. The Wall Street Journal picked it up. Fast Company put her on the 100 Most Creative People in Business list in 2018. She was not making arguments — she was publishing evidence, and the industry had nowhere to hide.

From MIT to the Kitchen and Back

Aline graduated from MIT around 2004 with a computer science degree — then did something almost no one in Silicon Valley would understand: she left to become a professional cook. For roughly three years, she worked in restaurant kitchens in New York City and San Francisco. Not as a hobby. As a career.

She came back to tech. Wrote code. Eventually ran hiring at Udacity, the online education platform, which put her in the room where hiring decisions actually get made — and gave her the vantage point she needed. She saw how arbitrary the process was. She saw how much smart, capable people were being screened out before they ever got a chance to show what they could do. In 2013, she launched her own recruiting firm, Aline Lerner LLC, with clients like Airbnb, Dropbox, and Lyft. And she started doing something unusual: interviewing candidates herself, then surfacing the strong performers directly to employers — regardless of what their resumes looked like.

That experiment became the seed of interviewing.io.

Build the Platform, Then Watch the Data Roll In

In 2015, Aline co-founded interviewing.io with fellow MIT alum Andrew Marsh. The core idea was almost offensively simple: let engineers practice technical interviews anonymously with professionals from top companies, then let the performance data — not the resume — speak.

Six years of data later, the results were unambiguous. Engineers who practiced on the platform roughly doubled their real interview success rates after about five sessions. Companies using interviewing.io converted candidates to hires at 10% — versus the industry standard of 0.5%, a 20x difference. And in the largest blind engineering hiring experiment she ran over six years: 46% of candidates who received offers at top companies didn't have top schools or top companies on their resumes — but performed as well or better than their pedigreed peers, and were twice as likely to accept offers.

Over one-third of Bay Area engineers have used interviewing.io. An engineer creates a new account every eight minutes.

COVID, Pivot, and a Series A Pitched Seven Months Pregnant

When COVID-19 hit in 2020, interviewing.io's employer-side revenue stopped almost overnight. Companies froze hiring. The platform that had been charging companies to find engineers now needed a new model. Aline pivoted — charging individual engineers for practice sessions instead. It was a harder sell and a bigger ask. It also worked. Recovery growth was strong enough to make the company VC-fundable.

In October 2021, interviewing.io raised a $10 million Series A led by M13 — six years after founding. Aline pitched it seven months pregnant. She gave birth three weeks early, during the final stages of negotiation. She's been public about the experience and specifically praised M13 for how they handled her pregnancy disclosure.

The Book That Rewrites the Rulebook

In January 2025, Aline co-authored Beyond Cracking the Coding Interview — the official sequel to Cracking the Coding Interview, co-written with Gayle Laakmann McDowell, Mike Mroczka, and Nil Mamano. It adds over 150 new problems, 13 new technical topics (sliding windows, prefix arrays, topological sort, and more), and draws on data from more than 100,000 FAANG mock interviews. It was built for today's market, not the one from fifteen years ago. Not a new edition. A new book.

She then made nine chapters freely available online. Because of course she did.

The Superscreener Theory

One of Aline's most concrete ideas for fixing hiring: find your "superscreeners." Every engineering org has a handful of interviewers whose candidates convert to hires at dramatically higher rates than others. Identify them. Route all initial phone screens through those people. The difference in outcomes is not marginal — it's structural.

She also argues that engineering leaders should personally make offer calls. Not HR. Not recruiting coordinators. The person who actually knows what the role entails and why the candidate would love it. The acceptance rate difference is significant enough to pay for the executive time many times over.

The LinkedIn Incident

In November 2023, LinkedIn deleted Aline's account. She had posted content critical of LinkedIn's certification and endorsement features — calling them low-signal, misleading noise in a system already drowning in it. The account vanished from search results. The company's followers evaporated. Her profile was gone.

It was only restored after investors intervened. She wrote about it. The irony of a platform nominally dedicated to professional development silencing someone for pointing out its flaws was not lost on her — or anyone who read the post.

The Mission Behind the Metrics

Aline Lerner's north star has been consistent since she was running her own recruiting firm in 2013: any smart person, regardless of how they look on paper, should be able to get in the door at a job they want and be evaluated on their merits. Not their school. Not their employer history. Not whether they could afford a resume coach. Their actual ability to do the work.

The pay-later program she piloted — "don't pay us a dime until you get a job" — had a 94% payback rate. The fellowship program gives free platform access to engineers from underrepresented backgrounds. The data from six years of blind experiments shows that the pedigree bias isn't just unfair; it's inefficient. Companies are leaving better candidates on the table because they're sorting by the wrong signal.

She's been saying this for over a decade. She has the data to prove it. And she built the platform that makes the alternative possible.