3 ms·
I read until the appendix but I find the intro wrong. From my experience big tech interviews are not for making sure that candidates don't write O(n^2) loops.
by siscia 2y ago
I read until the appendix but I find the intro wrong.
From my experience big tech interviews are not for making sure that candidates don't write O(n^2) loops. Code reviews are for that, some of the times.
Those interviews are there because we haven't figured out a better way to interview yet and this seems somehow correlated with the job. I don't think it is correlated, but it looks like it is!
Those interviews have also the hidden characteristics (your call of it is an plus or a minus) to select for quite confident engineers that usually can talk and defend their opinion. Which is quite useful in big tech.
For the reason why this does not get fixed.
It is not because no-one looked. People in the team know. We all know.
But we are being told not to focus on them.
With scales comes cost of running the whole machinery AND opportunity cost of not having the next product launched. With the scale of big tech, the opportunity cost easily dominate the cost of 1% increase in performance.
People don't realize it, but it is working well and as designed.
It is wasteful? Absolutely.
It makes a shit tons of money? Definitely!
- ilrwbwrkhv 2y agoA simple better way of interviewing is to simply ask the candidate to code review a piece of code. Works much better than any algorithmic exercise and gives you the fastest results in the least time for both the company and the candidate. But most companies are cargo cults. For example Stripe copies Airbnb which in turn copies Google.
- latkin 2y agoI usually find this author's posts pretty insightful, but this one was a miss for me. Just a rambling mishmash of ranting and humblebragging. The main point (I think? Hard to extract a thesis from this one besides "algo interviews bad") doesn't even hold up: "People say algo interviews reduce costly algo issues in production, but these issues still happen in production, therefore algo interviews don't work/those people are wrong." This conclusion doesn't follow. Who's to say that there wouldn't be twice as many, or twice as severe, algo problems if companies didn't interview this way? I'm not asserting that's the case, but it's consistent with the data points provided. Outside of official HR statements, perhaps, I don't think anybody pretends leetcode-style interviews are actually ideal for evaluating corporate software engineer candidates. Everyone knows they are deeply flawed and provided limited value. They persist because they seem to be the least worst option, all things considered.