Recently, someone asked us how you know you’re ready to succeed in a Facebook/Amazon/Apple/Netflix/Google (FAANG) interview. It’s an interesting question, and one I’m sure many of you job seekers out there are wondering. Internally, we have our own beliefs, but we wanted to see if we could answer this question more objectively. So we set off on a journey to acquire data to try answering it.
Recursive Cactus has been working as a full-stack engineer at a well-known tech company for the past 5 years, but he’s now considering a career move. Over the past 6 months, Recursive Cactus (that’s his anonymous handle on interviewing.io) has been preparing himself to succeed in future interviews, dedicating as much as 20-30 hours/week plowing through LeetCode exercises, digesting algorithms textbooks, and of course, practicing interviews on our platform to benchmark his progress. Recursive Cactus’s typical weekday schedule Time Activity 6:30am – 7:00am Wake up 7:00am – 7:30am Meditate 7:30am – 9:30am Practice algorithmic questions 9:30am – 10:00am Commute to work 10:00am – 6:30pm Work 6:30pm – 7:00pm Commute from work 7:00pm – 7:30pm Hang …
One of the things that sucks most about technical interviews is that they’re a black box—candidates (usually) get told whether they made it to the next round, but they’re rarely told why they got the outcome that they did. Lack of feedback, or feedback that doesn’t come right away, isn’t just frustrating to candidates. It’s bad for business. We did a whole study on this. It turns out that candidates chronically underrate and overrate their technical interview performance, like so: Where this finding starts to get actionable is that there’s a statistically significant relationship between whether people think they did well in an interview and whether they’d want to work with you. In other words, …
If you say the words “there’s a pipeline problem” to explain why we’ve failed to make meaningful progress toward gender parity in software engineering, you probably won’t make many friends (or many hires). The pipeline problem argument goes something like this: “There aren’t enough qualified women out there, so it’s not our fault if we don’t hire them.” Many people don’t like this reductive line of thinking because it ignores the growing body of research that points to unwelcoming environments that drive underrepresented talent out of tech: STEM in early education being unfriendly to children from underrepresented backgrounds, lack of a level playing field and unequal access to quality STEM education (see this study on …
We ran the numbers, and there really is a pipeline problem in eng hiring. Read more »
Whether you’re a recruiter yourself or an engineer who’s involved in hiring, you’ve probably heard of the following two recruiting-related metrics: time to hire and cost per hire. Indeed, these are THE two metrics that any self-respecting recruiting team will track. Time to hire is important because it lets you plan — if a given role has historically taken 3 months to fill, you’re going to act differently when you need to fill it again than if it takes 2 weeks. And, traditionally, cost per hire has been a planning tool as well — if you’re setting recruiting budgets for next year and have a headcount in mind, seeing what recruiting spent last year is …
Hello everyone, my name is Atomic Artichoke, and I’m the newest employee of the interviewing.io team, having joined a couple months ago as a Data Scientist. Atomic Artichoke isn’t my real name, of course. That’s the pseudonym the interviewing.io platform gave me, right before I took my final interview with the company. If you’ve never used interviewing.io before (and hey, if you haven’t already, why not sign up now?), it’s a platform where you can practice technical interviewing anonymously with experienced engineers (and do real job interviews anonymously too). When it’s time to interview, you and your partner meet in a collaborative coding environment with voice, text chat, and a whiteboard (check out recordings of …
Can fake names create bias? An exploration into interviewing.io’s random name generator Read more »
At the end of the day, money is a huge driver for the decisions we make about what jobs to go after. In the past, we’ve written about how to negotiate your salary, and there are a lot of labor statistics and reports out there looking at salaries in the tech industry as a whole. But as with many things in eng hiring, there’s very little concrete data on whether technical interview performance plays a role in compensation offers. So we set out to gather the data and asked our users who had gone on to successfully get jobs after using our platform to share their salary info. With our unique dataset of real coding …
