interviewing.io Blog

better interviewing through data
Quote from http://blog.interviewing.io/

Writing Good Code is like Writing a Novel
Quote from https://hackernoon.com/writing-good-code-is-like-writing-a-novel-33973645be01

When I put above two ideas together, I realized following.

If you were running a publishing house and needed to a writer to publish your next book, how do you go about picking the writer?

You usually look at the body of work from the writer. You read the books the author wrote in the past to decide if the writer is any good.

You don't decide if the writer is good or bad from just

1) reading a page of resume and

2) asking him to write out a clever sentence on a whiteboard in the middle of the interview.

That tiny slice of data is useless for deciding on anything. If you are all about deciding based on data, why would you make an employment decision based on the most tiny fraction of the actual data (resume and 30 min interview)?

Programming/IT/DevOps workers now have ample ways of showing past work. Often it's personal projects in github or a blog with tutorials on how to secure servers. These are far better tools to measure a candidate. Just like reading books by an author is a far better way to predict how that author's works would turn out.

And my personal experiences have shown me that majority of interviewers don't read my blog or my github. I find out in interviews that some don't know it's there. It's at the top of my resume for a reason.

And no one's asked…

Terrific analysis, thank you! I love reading your posts.

I also think that the dynamics differ based on the mode of interviewing viz. Faceless (like http://interviewing.io does) vs face-ful (like normal onsite or video interviews, or the way http://interviewkickstart.com does).

I have an observation to share for the latter kind of interviews:

When I was hiring at Box for one of my teams (or even now), I'd generally go towards the end, to meet the candidate and talk to them about how the day went. I'd ask them about specific interviews, the interviewers and how they think they did on the given problems.

I found that it was surprisingly rare to get a candidate who was aware enough of how things went (positive or otherwise).

Digging into it, I realized that the correlation of what the candidate perceived, was simply to how expressive the interviewER was. If the interviewer was friendly and chatty, the candidate would feel like they have done well (or at least forgivably well). If the interviewer was more silent, the candidate would feel they haven't done very well.

Now that I do interview training for a living, I try and hammer this point home: that interviews are like a date. It depends a lot on the other person (in this case, the interviewer). Understanding this well often works like magic.

I was in a similar situation 3 years ago. Like other people said, I'd focus on growth-stage startups (Stripe, Checkr, Airbnb, Opendoor, etc). I found their interviews more practical and approachable than Facebook, Google, etc. Most of the places I interviewed had me writing and running real code on my own computer.

I spent about a month prepping with:

* Cracking the Coding Interview

* https://www.interviewcake.com: curated set of ~50 questions with excellent step-by-step hints that don't immediately spoil the whole problem. Well worth the $250 — I didn't do any Leetcode-style problems aside from these.

* https://interviewing.io: Real, anonymous phone screens. This was amazing for me because I hadn't done a technical interview in 4 years and going through a real interview with a real person is totally different than practice problems.

Companies in SF will cover your costs to interview and usually will give you a relocation bonus as well, so don't discount moving down here :)

interviewing.io | Sales Manager | SF | ONSITE, FULL-TIME | https://www.interviewing.io

interviewing.io helps companies hire great engineers. We provide candidates with free, live, rigorous practice interviews and use data from these interviews to identify top performers, who can then talk to companies anonymously right on our platform. We’ve hired for Facebook, Uber, Twitch, Lyft, Yelp, and more.

You’d be the 1st full-time sales person on the team and our 7th overall hire, so in addition to the nice equity upside for being an early employee, there’s tremendous opportunity to make a big impact, not just on the company, but on the direction of the whole hiring industry. So, if you’ve ever wanted to take hiring from a Kafkaesque resume-ridden shit-storm into a rational, delightful process for competent humans, this is your chance!

Email nico@interviewing.io if interested.

From: https://interviewing.io/blog/why-ai-cant-do-hiring#user-content-fn-4

"The future of technical assessments, in the age of generative AI is a noble and difficult topic, though, and it’s something we’ll tackle in a future post."

This is exactly what we have built (are now applying to YC as well).

Can share more details soon (we are integrating some much needed love on our site), but we are:

- FAANG + Startup + Fintech founders

- super technical and our expertise is in Product Engineering + ML-for-products

- keeping everything 3.5-turbo based free, and probably will have to charge for 4

Our early users (Google/Meta SWEs and a few small AI startups) are pretty shocked with the results they are seeing, which they consider to be "as good if not better than human interviews + feedback".

I'll post here next week with a link to try!

November of 2022 is when the Facebook and Amazon had their big layoffs, and Google, Microsoft, and Salesforce etc. followed in the two months after.

I haven't had a recruiter contact me on Linkedin for over a year, in 2022 and before I was being contacted by recruiters on Linkedin all the time.

Companies have different modes - mass layoffs, small layoffs, no hires, only hiring replacements, a few new slots open, many slots open. In early-mid 2022 we were in the last category and it's moved to one of the other modes for most companies, except for LLM hires.

There are different sources of information, I found this helpful ( https://interviewing.io/blog/when-is-hiring-coming-back-predictions-for-2024 ) from a company with some insights into tech hiring.