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Google interview notes, page 76 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 engineer phone screen, one coding problem the whole time. The problem was more interesting than I expected but I second-guessed my data structure choice for way too long.
  • Design a class for a conveyor belt that ingests items one at a time by size. Implement an add(size) method that, as soon as any three recorded items form a group where the difference between the largest and smallest is below a given threshold, returns that triple.

“I kept the items in a sorted list using bisect insertion, so each add is O(log n).”

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Google·Software Engineer·Onsite - System Design / Architecture

May 2026
Google system design round for a software engineer role. The question was a classic resource-management problem dressed up as log storage, and the O(1) deletion constraint is what made it actually interesting.
  • Design a log management system that enforces a maximum log count, automatically evicts the least important or oldest logs when capacity is exceeded, and can locate the log to delete in O(1) time.

“The capacity enforcement part felt straightforward at first but the O(1) deletion requirement is where it gets tricky.”

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Google·Software Engineer·Onsite - Product Sense / Strategy

May 2026
Interviewed for a product designer role at Google, got asked a research ops question that I didn't see coming at all.
  • How would you recruit participants for usability testing if you had no access to an existing client base or internal users?

“I fumbled this a bit.”

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Google·Data Scientist·Technical Phone Screen

May 2026
Google DS interview, one big causal inference question that took up basically the whole session. The depth they expected was pretty serious, covering everything from identification assumptions to sensitivity analysis. Felt like a take-home problem crammed into a live conversation.
  • You have observational data on a recommender feature where users self-selected into treatment. Walk through how you'd use propensity score matching to estimate the causal effect on 7-day retention, covering identification assumptions, covariate selection, matching mechanics, balance checks, ATE vs ATT, uncertainty quantification, and what you'd do if PSM falls short.

“This was a lot to hold in your head at once.”

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Google·Software Engineer·Onsite - Coding / Algorithms

May 2026
Google onsite coding round in Mountain View, mahjong hand detection as the main problem. The prompt was intentionally vague which meant the first few minutes were basically just clarification questions before any code got written.
  • Given an array of 14 integers representing mahjong tiles, determine whether the hand is a winning hand (one pair plus four melds, where each meld is either a triplet or a run of three consecutive ranks).

“The problem statement they give you is basically nothing.”

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Google·Product Manager·Onsite - Product Sense / Strategy

May 2026
Interviewed at Google for a product role and got hit with a classic product sense question. Not much else to report, pretty short session from what I can tell.
  • How would you improve a product you use every day?

“Picked Google Maps because I figured I'd have enough to say.”

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Google·Software Engineer·Technical Phone Screen

May 2026
Google SWE interview with a tree serialization problem. Pretty focused session, just the one problem but they pushed on edge cases more than I expected.
  • Implement encode and decode functions for a general N-ary tree (a tree where each node can have any number of children).

“Spent too long second-guessing the serialization format.”

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Google·Machine Learning Engineer·Technical Phone Screen

May 2026
Google ML Engineer coding round where I had to implement a Transformer with SwiGLU from scratch. Pretty deep dive into the architecture, feedback came back positive so I'll take it.
  • Implement a Transformer architecture from scratch, including Multi-Head Attention and a SwiGLU-based feed-forward block with a parallel gating mechanism.

“This was a full implementation question, not just explain-the-concept.”

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Google·Software Engineer·Technical Phone Screen

May 2026
Google coding interview, one algorithmic problem, nothing fancy about the setup but the problem itself had some gotchas if you weren't careful about duplicates.
  • Given an array of integers and a target sum, find all unique quadruplets (four elements) in the array that add up to that sum.

“My first instinct was to just nest four loops and call it a day, which obviously isn't going to fly at Google.”

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Google·Software Engineer·Onsite - System Design / Architecture

May 2026
Google EM interview with a single system design question about migrating data between two distributed systems. Pretty open-ended, which I wasn't fully prepared for.
  • Design a solution to migrate data from Cassandra in one system to another system.

“I jumped straight into talking about batch jobs and CDC (change data capture) without first clarifying what 'System Y' actually needed.”

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