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Google interview notes, page 52 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

May 2026
Google SWE coding round, one question about counting the number of ways to split a string. Pretty light on details but the problem itself had some meat to it.
  • Given a string, count the number of ways to split it into parts according to some defined criteria.

“Classic combinatorics-meets-string-manipulation type problem.”

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Google·Product Manager·Recruiter / HR Screen

May 2026
Interviewed for a PM role at Google and basically got hit with the classic 'why do you want to be a PM' question. Short screen, nothing wild.
  • Why do you want to be a Product Manager?

“Pretty standard opener but I still fumbled it a bit.”

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Google·Product Manager·Onsite - Product Sense / Strategy

May 2026
PM interview at Google for their Wing drone delivery product. Just one question but it's a beast, basically asking you to think through building an entirely new product from scratch in a space most people know nothing about.
  • You're a new PM at Google Wing. How do you approach building the product?

“This is the kind of question where you can go in a hundred directions and that's kind of the problem.”

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Google·Machine Learning Engineer·Technical Phone Screen

May 2026
Went through a coding round for an MLE role at Google, got a dynamic programming problem that looked like a math puzzle at first glance. Not the most brutal interview I've had but it required more careful thinking than I expected.
  • Given an array of coin denominations and a target amount, return the total number of combinations of coins that sum to that amount. You have unlimited coins of each type. Return 0 if it's not possible.

“Classic unbounded knapsack variant but I kept second-guessing whether order mattered (it doesn't, combinations not permutations).”

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Google·Software Engineer·Hiring Manager Screen

May 2026
Interviewed at Google, got asked one of those deceptively soft questions that I probably didn't take seriously enough.
  • What does a great day at work look like for you?

“I rambled about shipping features and collaborating with teammates, which felt fine in the moment but probably came across as generic.”

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Google·Software Engineer·Onsite - Coding / Algorithms

May 2026
Google SWE coding round, pretty much a pure algorithms session. One main problem with a follow-up that I half-expected but still fumbled a bit on the optimization side.
  • Given an integer array of length N, return all pair sums nums[i] + nums[j] for every i < j. The output can contain duplicates if the same sum is reachable through different index pairs.
  • Follow-up: how would you efficiently return only the top K largest pair sums from that same array?

“Jumped straight to the nested loop solution which is fine, it's O(N^2) and the output is literally N*(N-1)/2 entries so there's not much you can do about space.”

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Google·Product Manager·Onsite - Product Sense / Strategy

May 2026
Google PM interview, one product sense question about a niche startup scenario. Pretty open-ended and I wasn't totally sure what angle they wanted.
  • You're the PM of a startup that connects musicians for collaborations. How would you define and measure success for that product?

“I went straight to engagement metrics and kind of rambled about DAUs and match rates before realizing I hadn't even defined what 'success' means for a two-sided marketplace like this.”

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Google·Software Engineer·Technical Phone Screen

May 2026
Google SWE coding round, one question about serializing and deserializing a list of strings. Pretty focused session, no fluff.
  • Write functions to serialize and deserialize a list of strings.

“Looks easy until you start thinking about edge cases.”

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Google·Machine Learning Engineer·Technical Phone Screen

May 2026
Google MLE interview with a meaty scenario question about model safety versus short-term metric gains. No fluff, just one deep situational question that made me realize how much of this job is actually about stakeholder politics and not just model accuracy.
  • Your model shows a statistically significant improvement in an A/B test, but your manager is worried it might hurt long-term user experience. How do you handle this, what metrics would you propose to track long-term impact, and what do you do if you can't quickly prove the model is safe?

“This one tripped me up more than I expected.”

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Google·Product Manager·Onsite - Product Sense / Strategy

May 2026
PM interview at Google that threw a ride-sharing product case at me. The question covered launch strategy, market sizing, and metrics all at once, which felt like a lot to juggle in one prompt.
  • You're the PM for Uber or Lyft and tasked with launching a scheduled rides feature. How do you approach the launch, size the market, and determine what metrics matter?

“This question is doing three jobs at once and I didn't realize that fast enough.”

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