Articles like this for me tend to vindicate Google's notorious hiring processes.
While it is true that for most people will not need to be able to whiteboard a binary tree inversion in their day to day, it seems like they expect their engineers to be able to throw themselves at any problem they're given and require them to be able to pivot in skillset quickly, and have an appreciation of all the developments going on around them so they can apply anything novel ideas developed internally to what they are currently working on.
In those cases, hiring based on sound knowledge of CS fundamentals seems like a good bet...
60k engineers is a pretty terrifying number though.
I'm skeptical nevertheless. In my experience, most programming is very different than r+d, which often does require significant concentrated training or even the smartest will spin their wheels.
It's hard to describe, but research (which the vast majority of ML remains) is something that even a sound knowledge of fundamentals might not remotely be enough.
Google's largely moved away from those BS questions. They just bias towards people who memorize answers on Leetcode, but aren't actually capable of producing anything.
Memorizing answers to algorithm questions is a poor time investment. I don't see a lot of people doing it. Most smart folks just learn how to design algorithms on the fly, that's much easier and more useful.
While it is true that for most people will not need to be able to whiteboard a binary tree inversion in their day to day, it seems like they expect their engineers to be able to throw themselves at any problem they're given and require them to be able to pivot in skillset quickly, and have an appreciation of all the developments going on around them so they can apply anything novel ideas developed internally to what they are currently working on.
In those cases, hiring based on sound knowledge of CS fundamentals seems like a good bet...
60k engineers is a pretty terrifying number though.