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Google interview notes, page 135 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·Software Engineer·Technical Phone Screen

Jun 2026
Google SWE coding round focused on building a word prediction system from scratch, with two follow-ups that pushed into probability and time complexity optimization. Pretty meaty for a single problem.
  • Design a word prediction system: write a training function that takes a list of sentences and tracks which words follow each word, then write a predictor function that takes a word and returns the most likely next word.
  • Follow-up: how would you optimize the predictor function to return the most likely next word in O(1) time instead of O(k)?
  • Second follow-up: modify the predictor to return a word sampled according to probability rather than always returning the top word. For example, 'I' should return 'am' 2/3 of the time and 'like' 1/3 of the time.

“The core problem wasn't bad.”

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Google·Software Engineer·Onsite - Coding / Algorithms

Jun 2026
Google SWE coding round with a digit DP problem that looks approachable until you actually read the constraints carefully. The 'Perfect Wake Number' definition has a few layers and getting all of them right under pressure is its own challenge.
  • Given an integer N (up to 10^18), count how many integers in [1, N] are 'Perfect Wake Numbers', where a Perfect Wake Number contains no zero digit, has no repeated digits, and no digit is sandwiched between two strictly larger neighbors.

“The third condition is the one that'll get you.”

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Google·Data Scientist·Technical Phone Screen

Jun 2026
Google data scientist technical screen, basically one meaty coding problem that touched NumPy, probability, and numerical correctness all at once. More involved than I expected for a phone round.
  • Using Python and NumPy, generate a 100x100 matrix of binomial draws (n=10, p=0.3) with a fixed random seed, then normalize each column to sum to 1. Handle any all-zero columns explicitly, verify column sums within floating-point tolerance, and provide time/space complexity plus a unit test.

“This question has a lot of layers and I underestimated it at first.”

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Google·Software Engineer·Onsite - Behavioral / Leadership

Jun 2026
Interviewed for a product design role at Google, just the one question from what I can tell. Pretty standard behavioral territory but it's the kind of thing that trips you up if you haven't thought it through.
  • How do you handle conflict that comes up during a project?

“I went with a real example from a cross-functional project where design and engineering had completely different priorities.”

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Google·Data Scientist·Technical Phone Screen

Jun 2026
Got a stats question at Google that sounds straightforward until you're actually in the room trying to explain it without sounding like you're reading off a textbook.
  • Can you explain the statistical concept of power and what drives it?

“I knew the definition but fumbled when trying to connect all the moving pieces together.”

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Google·Software Engineer·Technical Phone Screen

Jun 2026
Interviewed for a security engineer role at Google and got asked a networking fundamentals question. Pretty standard stuff, nothing too wild.
  • Can you explain what TCP/IP is?

“Knew this one but still fumbled the explanation a bit.”

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Google·Product Manager·Onsite - Product Sense / Strategy

Jun 2026
Google PM interview, got a classic industry disruption question that sounds open-ended but has a lot of hidden structure to it.
  • How would you disrupt the car wash industry?

“I blanked for a second because it felt random.”

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Google·Software Engineer·Hiring Manager Screen

Jun 2026
Interviewed for a product designer role at Google, just the one question about Android experience before things wrapped up.
  • Can you walk me through your background working with Android?

“Pretty open-ended, which I wasn't fully prepared for.”

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Google·Machine Learning Engineer·Onsite - System Design / Architecture

Jun 2026
Google ML design round for an MLE role. One question, pretty focused: build a system that identifies leaf categories from a photo using retrieval and ranking. Felt like a reasonable scope for the time given but there's a lot of ground to cover.
  • Design a mobile app feature that takes a photo of a leaf and identifies its category, using a retrieval-based approach combined with a ranking model.

“The retrieval plus ranking framing is the interesting part here.”

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Google·Software Engineer·Onsite - Behavioral / Leadership

Jun 2026
Went through a behavioral round at Google for a software engineer role. All three questions were conflict-related, which I wasn't expecting to be the whole focus. Left feeling like I probably over-explained on at least one of them.
  • How do you handle conflict with a colleague?
  • How do you handle conflict with your manager?
  • What would you do if a teammate is not getting along with the rest of the team?

“Gave a pretty textbook answer about talking it out directly before looping in anyone else.”

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