Why AI can’t do hiring

The recent exciting and somewhat horrifying inflection point in AI capability tipped me into writing this blog post.

**I simply don't believe that AI can do hiring. **My argument isn't about bias (though bias is a real problem) or that it's technologically impossible. It's just that the training data simply isn't available.

Most people believe that if you can somehow combine what's available on LinkedIn, GitHub, and the social graph (who follows whom on Twitter etc.), you'll be able to find the good engineers who are actively looking and also figure out what they want. This is wrong. None of those 3 sources are actually useful. At the end of the day, you can’t use AI for hiring if you don’t have the data. And if you have the data, then you don’t strictly need AI.

Does posting Open To Work on LinkedIn help or hurt? A tale of two labor markets.

If you’re a software engineer who’s on the market, should you list yourself as OpenToWork? Does doing so carry a negative signal? And with the recent deluge of layoffs at tech companies, has the meaning of OpenToWork changed?

TL;DR It used to be bad. Now, it's not. Moreover, it's clear to us that the people who were laid off in the 2nd half of 2022 and 2023 so far are great... and that, by and large, these layoffs were indeed NOT based on performance.

Why you shouldn’t list certifications on LinkedIn

People often suggest that interviewing.io should create a certification that our users can post on their LinkedIn profile, e.g., something like “Top 10% performer on interviewing.io”. Presumably, these certifications would signal to recruiters that this person is a good engineer and worth reaching out to and should carry more signal than where said person went to school or worked previously.

I've always thought certifications were a terrible idea, and I’ve resisted building them. Now, we've finally dug into the data to see if my hatred of them holds water. TL;DR it does.

We have the best technical interviewers on the market. Here's how we do it.

interviewing.io is an anonymous mock interview platform and eng hiring marketplace. We make money in two ways: engineers pay us for mock interviews, and employers pay us for access to the best performers. This means that we live and die by the quality of our interviewers in a way that no single employer does – if we don’t have really well-calibrated interviewers, who also create great candidate experience, we don’t get paid.

In a recent post, we shared how, over time, we came up with two metrics that, together, tell a complete and compelling story about interviewer quality: the candidate experience metric and the calibration metric. In this post, we’ll talk about how to apply our learnings about interviewer quality to your own process. We’ve made a bunch of mistakes so you don’t have to! It boils down to choosing the right people, tracking those 2 metrics diligently, rewarding good behavior, and committing to providing feedback to your candidates.

How software engineering behavioral interviews are evaluated at Meta (from an ex-Meta manager)

Hi, I’m Lior. I spent close to five years at Meta as a software engineer and engineering manager. During my time there I conducted more than 150 behavioral interviews. In this post, I’ll be sharing what Meta looks for in a behavioral interview, and how we evaluated candidates.

Why giving feedback (whether it’s good or bad) will help you hire

Giving feedback will not only make candidates you want today more likely to join your team, but it’s also crucial to hiring the candidates you might want down the road. Technical interview outcomes are erratic, and according to our data, only about 25% of candidates perform consistently from interview to interview.

How many engineers have gotten laid off in 2022 and 2023 so far?

I recently ran a Twitter poll asking my followers to estimate how many engineers had been laid off from US-based startups and tech companies in 2022 and 2023 so far. Most people overestimated the number by an order of magnitude. Here's what we did to get to the actual number.

Our business depends on having the best interviewers, so we built an interviewer rating system. And you can too.

interviewing.io is an anonymous mock interview platform and eng hiring marketplace. Engineers use us for mock interviews, and we use the data from those interviews to surface top performers, in a much fairer and more predictive way than a resume. If you’re a top performer on interviewing.io, we fast-track you at the world’s best companies.

We make money in two ways: engineers pay us for mock interviews, and employers pay us for access to the best performers. To keep our engineer customers happy, we have to make sure that our interviewers deliver value to them by conducting realistic mock interviews and giving useful, actionable feedback afterwards. To keep our employer customers happy, we have to make sure that the engineers we send them are way better than the ones they’re getting without us. Otherwise, it’s just not worth it for them.

This means that we live and die by the quality of our interviewers, in a way that no single employer does, no matter how much they say they care about people analytics or interviewer metrics or training. If we don’t have really well-calibrated interviewers, who also create great candidate experience, we don’t get paid.

In this post, we’ll explain exactly how we compute and use these metrics to get the best work out of our interviewers.

You now need to do 15% better in technical interviews than you did at the start of 2022 (and the bar will keep rising).

interviewing.io is a technical mock interview platform and technical recruiting marketplace, so we have a ton of useful data around technical interviewing and hiring. One of the most useful pieces of data in the current climate is the ever-changing technical interview bar – throughout 2022, it’s gotten progressively harder to pass technical interviews, and it’s only going to keep getting harder. We crunched the numbers and came up with a running index that quantifies where the eng bar will be, as a function of open tech jobs. The bar is clearly going up.

How much have 2022 layoffs affected engineers vs. other departments? We dug into the data to find out.

Over the past few months, I’ve seen a number of fear-mongering pieces in the press about how the recession is driving tech layoffs and how tech employees (and engineers specifically) are losing their leverage as a result. The problem is that “tech” can mean anyone working at a tech company. You’re an engineer? Of course, you’re tech. You do ops? Great, you’re tech. You do marketing? You, too, are tech! These are all critical roles at tech companies, and what I take umbrage with isn’t the decision to label non-engineers as tech employees. It’s deliberately misleading your audience by implying that “tech” refers to engineers specifically.

I don’t like imprecision, and I really don’t like fear-mongering. So, we at interviewing.io dug into the data to see if engineers do indeed have a reason to fear.