My Google Recruitment Journey (Part 1): Brute-Forcing My Algorithmic Ignorance
March 23, 2026
About 2 months ago, an email from xwf.google.com dropped into my inbox,
referencing an application from a year prior that I even forgot about.
My initial classification was that it is not possible and that this is just spam.
But after the screening call, the reality hit: I will have two online interviews (one technical, one behavioral) in just a week.
And not just a regular interview to another company, these will be interviews for a company
that I still consider as one of the top-of-the-world factory of engineers.This was a critical state. I’ve worked as a software developer in telecommunications for a few years, focusing on high-level abstraction:
routing, message processing, and writing business logic.
In my hobbyist gamedev projects, even though sometimes I liked to make some pathfinding algorithm or to do a CPU 3D rasterizer by hand,
at the end of the day my metric for success was simple: if it runs at >60 FPS without drops, it ships.
One of the critiques you frequently see of AI across a broad range of applications, from education to software engineering to pick a subject area, is that by relying on them we dumb ourselves down. We don’t do the work. We cheat. We get to the solution without doing the work that helps us understand the solution.
In my increasingly significant experience, this is a philosophical or even potentially a theological concern rather than an empirical one. That’s not to say you can’t do those things with these technologies. But on the other hand, as the following article will demonstrate, you can do quite the reverse.
Over the Christmas New Year period, I worked my way comprehensively through Anil Ananthaswamy highly recommended Why Machines Think, a history of machine learning and its mathematics.
I worked my way through it by giving Claude every section as I read it and clarifying things that I didn’t understand, asking Claude to question me as to my understanding, and I came away, I think, with an enormous uplift in my understanding of machine learning.
Now onto this article and the anecdata.
The technical interviews for Google have long been considered incredibly exacting and relying very much on detailed understanding of computer science concepts.
Unless you’re fresh out of university, it’s likely you’re going to have to extensively refresh your knowledge of many computer science concepts, because you probably haven’t been using many, if any of them, extensively in your day-to-day work.
Here’s a first-hand account of using LLMs to prepare for a Google technical interview in a couple of weeks. There might be some strong pointers for you, not just if you want to innovate in Google, but if you want to start thinking about how you can use these technologies as ways to sharpen and deepen your knowledge.
