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Google interview notes, page 104 of 215

FAANG / Big Tech

Most of these Google write-ups come from Software Engineers (1,167), then Product Managers (686) and Data Scientists (125). The most reported rounds are the product sense round (667), the technical phone screen (660), the behavioral / leadership round (252) and the onsite coding round (214).

2146 interview notes · updated Oct 2026

Google·Machine Learning Engineer·Technical Phone Screen

May 2026
Google ML interview, got asked a pretty foundational question about random forests and ensemble methods. Nothing too wild but it made me realize how easy it is to fumble the basics when you're nervous.
  • Explain how random forests work and what makes ensemble methods different from single models.

“I knew this cold in theory but started rambling about bagging before actually defining what an ensemble is, which felt backwards in hindsight.”

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Google·Data Scientist·Technical Phone Screen

May 2026
A Google data scientist interview that went deep into ML system design for a B2B churn prediction problem. The question had a lot of moving parts and I definitely didn't cover everything cleanly, but it was a good conversation.
  • You're a data scientist at a video-conferencing company. There's been a spike in call disconnects, and you need to predict which enterprise customers will renew their contracts. Walk through your full modeling approach, from defining the label to translating model scores into business actions.
  • When would you choose logistic regression over gradient-boosted trees or a deep model for this churn prediction task, and when would you go the other direction?
  • How would you evaluate this model beyond just looking at AUROC?
  • How do you translate model scores into actual customer success actions, given capacity constraints and expected ROI?
  • If you had to ship a first version of this model quickly, what's the minimal viable feature set you'd use and why?

“This question is basically three questions in a trenchcoat.”

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Google·Machine Learning Engineer·Technical Phone Screen

May 2026
Interviewed at Google for a machine learning role, got a pretty classic algorithm selection question that sounds straightforward until you're actually sitting there trying to justify your choices out loud.
  • How would you go about selecting the right machine learning algorithm for a given system?

“I started talking about data size and feature types, which was fine, but I kind of rambled into bias-variance tradeoff territory without tying it back to a concrete decision framework.”

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Google·Software Engineer·Technical Phone Screen

May 2026
Google SWE coding round with one problem that looked straightforward but had enough edge cases to keep me busy. The core idea was grouping numbers by shared digits, which sounds easy until you actually sit down and think through all the cases.
  • Given an array of two-digit numbers (up to 100 elements), find the largest subset where every number in the subset shares at least one digit that appears in all other numbers of the subset. For example, [55, 58, 25, 45] can form a group of 4 since the digit 5 appears in every member, but [55, 66, 77] cannot form such a group.

“My first instinct was to think of this as a graph problem and I started going down that path before realizing there's a cleaner angle.”

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Google·Software Engineer·Online Assessment (OA)

May 2026
Google SWE coding round, got a trie implementation problem. Pretty standard data structures territory but the constraints were tight enough that a sloppy implementation would've timed out.
  • Implement a Trie (prefix tree) that supports insert, exact-word search, and prefix lookup operations, with up to 200k queries and 2 million total characters.

“The core structure wasn't the hard part, it's making sure your node traversal doesn't do anything dumb at scale.”

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Google·Software Engineer·Hiring Manager Screen

May 2026
Interviewed for a SWE role at Google, pretty standard stuff. One question about past projects, not much else to report.
  • Walk me through some of your past projects.

“Classic opener.”

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Google·Software Engineer·Onsite - Coding / Algorithms

May 2026
Google SWE coding round, one problem the whole session. Binary search on answer type, which I've seen before but always fumble the feasibility check under pressure.
  • Given an array of task workloads and a target time threshold T, find the minimum number of workers needed so that you can partition the array into contiguous segments where no segment's sum exceeds T.

“I recognized the binary search on answer pattern pretty quickly, which felt good.”

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Google·Software Engineer·Technical Phone Screen

May 2026
Google SWE interview with a graph problem that looks straightforward but has a neat trick buried in it. The solution clicked eventually but I spent way too long thinking about it the wrong way first.
  • You have n players with unique hidden rankings from 1 to n. A higher-ranked player always beats a lower-ranked one. Given a list of match results, find every player whose exact rank can be determined, and return their ranks.

“My first instinct was to try topological sort and I kept going down that path longer than I should have.”

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Google·Product Manager·Onsite - Product Sense / Strategy

May 2026
Google PM interview with a product sense question about a bad software experience. Pretty standard but it's the kind of question that sounds easy and then you're sitting there second-guessing every app you've ever used.
  • Describe a software product you've had a frustrating experience with recently and explain what made it feel broken from a user's perspective.

“I blanked for a second and picked something too obscure trying to seem interesting.”

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Google·Software Engineer·Technical Phone Screen

May 2026
Google SWE interview focused entirely on a computational geometry problem about square overlaps on a 2D plane. No coding required, just a verbal walkthrough of multiple approaches and their trade-offs. Pretty niche problem but the discussion format actually made it more interesting than I expected.
  • Given N axis-aligned squares on a 2D plane, each defined by a bottom-left corner and side length, find the maximum area covered by the largest simultaneous overlap of any subset of these squares. Discuss multiple approaches verbally, including their time and space complexity trade-offs.

“The brute force angle was easy enough to talk through, O(N^2) pairwise intersections, nothing fancy.”

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