A marketplace dressed as a stopwatch.
From the outside, interviewing.io looks like a prep tool. It is not. Prep is the front door. The building is a hiring marketplace - one that flipped the lights off on purpose.
Engineers come to practice. They book a mock interview with someone who has actually given interviews at Meta or Google or OpenAI. They talk through a problem. They get a score. If the score is high enough, they unlock a second door. Behind that door are real first-round interviews at real companies - Uber, Lyft, Quora, Asana - still anonymous. The company sees the performance before it sees the person. By the time anyone learns your name, you have already proven you can do the job.
It is a small inversion that produces a very large result. Roughly half of interviewing.io's placed candidates would not have made it past a resume screen at the same company. They had the wrong school, or the wrong job titles, or a five-year gap, or a name that pattern-matched poorly to the people doing the screening. The platform turned the lights off and the bias went with it.
Founded by the person hiring was broken for.
Aline Lerner did not start out as an engineer. She started out as a technical recruiter, which is to say: she started out watching qualified people get filtered out for reasons that had nothing to do with whether they could write code. She wrote about it - bluntly, with data, on a personal blog at blog.alinelerner.com - and the blog became required reading for an entire generation of people trying to fix engineering hiring.
Then she built the company the blog implied. interviewing.io launched in 2015 out of San Francisco. It went out of beta. It raised seed money from Susa Ventures, Kapor Capital and others. In October 2021 it raised a $10 million Series A led by M13. The pitch to investors was unfussy: technical hiring is a coin flip dressed up as a science, and we have ten years of receipts to prove it.
Five things, one philosophy.
If you are an engineer, the platform gives you five doors:
Mock interviews with senior, staff and principal engineers from FAANG, FAANG+ and frontier AI labs. Real interviews at hiring companies once you've proven you can solve their problems. An AI Interviewer for the days you do not want to talk to a human - it runs FAANG-style coding and system design loops and grades like a staff engineer with no patience. Mentorship programs specifically tuned for Amazon, Google and Meta. And the Learning Center: company-by-company hiring guides, a much-cited blog, and an enormous library of replays of other people's interviews so you can watch how the smart move actually sounds.
The catch is the catch.
There is a small, beautiful gating mechanism. The interviewers running mock sessions are themselves rated. Only the top five percent of them are allowed to conduct interviews on behalf of paying companies. The platform applies the same merit test to its supply side that it applies to its demand side. The result is a feedback loop that quietly compounds: better interviewers attract better candidates, better candidates produce better signal, better signal attracts more companies.