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Google interview notes, page 120 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·Onsite - Product Sense / Strategy

May 2026
Got a product sense question at Google for a PM role on the Android team. Pretty open-ended hypothetical about a fictional hardware capability, which I wasn't expecting going in.
  • Imagine a new microchip makes it possible for smartphones to detect and process smells. As the PM for Android, how would you respond to this technology becoming available, and what use cases would you prioritize?

“I spent probably too long trying to think of a clever single use case instead of just mapping out a landscape of possibilities first.”

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Google·Software Engineer·Onsite - System Design / Architecture

May 2026
Staff-level system design round at Google focused entirely on a deep retrospective of a complex system I'd built. Less about whether it worked and more about whether I understood why certain decisions were wrong in hindsight.
  • Walk me through the most complex system you've built. What were the requirements and constraints at the time, and why did you design it the way you did?
  • Which architectural alternatives did you consider and reject, and what was your reasoning at the time?
  • What bugs, scalability problems, or operational issues came up after the system was in production?
  • Looking back, what decisions turned out to be wrong, and what would you do differently now?
  • How have these past experiences changed the way you approach system design problems today?

“I'd prepared a system I was genuinely proud of, which in retrospect made me too defensive early on.”

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Google·Software Engineer·Technical Phone Screen

May 2026
Google interview question about finding statistical evidence for a conversion rate. Not much context to go on but it's a classic experimentation-type problem that shows up in data or PM roles.
  • How would you find statistical evidence to determine whether a conversion rate is meaningful or significant?

“This is the kind of question where you need to talk through hypothesis testing pretty carefully.”

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Google·Software Engineer·Onsite - System Design / Architecture

May 2026
Google SWE system design round, one question the whole time: design a load balancer. Pretty sparse on details but that's kind of how these go.
  • Design a load balancer from scratch.

“There's a lot hiding under this question.”

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Google·Software Engineer·Technical Phone Screen

May 2026
Google SWE interview that centered on a generalized Tic-Tac-Toe design problem. The twist of supporting k players on an n x n board with a fixed 3-in-a-row win condition made it less straightforward than the classic version, and the expectation to discuss complexity before touching code caught me a bit off guard.
  • Design a Tic-Tac-Toe system that supports k players on an n x n board, where a player wins by forming exactly 3 consecutive marks in a straight line. After each move, the system should return the current game status.

“My first instinct was to scan the whole board after every move, which they pushed back on pretty quickly.”

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Google·Machine Learning Engineer·Technical Phone Screen

May 2026
Google ML Engineer interview with a project deep-dive. One question, lots of follow-ups, and you really have to know your own work inside out.
  • Walk me through a project you're most proud of: what was the problem, what were you trying to achieve, what did you personally own, what technical choices did you make and why, what stack did you use, what were the measurable outcomes, what risks did you have to manage, and what would you do differently?

“This is the kind of question that sounds easy until you're actually in it and realize you've been vague about your own work for years.”

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Google·Software Engineer·Onsite - Coding / Algorithms

May 2026
Google SWE interview with a graph theory problem disguised as a grid puzzle. Took me a bit to see what was actually going on under the hood.
  • You have an m x n binary grid representing a roof where 0s are leaking cells and 1s are intact. You can place planks that cover an entire row or an entire column. What is the minimum number of planks needed to cover every leaking cell?

“I spent way too long thinking about this as a greedy coverage problem, like just pick the row or column with the most zeros and repeat.”

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Google·Software Engineer·Technical Phone Screen

May 2026
Google SWE coding round with a sliding window / sorted structure problem about packing items from a conveyor belt into triplets. The problem sounded deceptively manageable at first glance but the online nature of it (streaming input, immediate output, removal) added real complexity.
  • Design a class that accepts a stream of integer item sizes and, whenever any three stored items satisfy max minus min being strictly less than a given threshold, immediately returns one such sorted triplet and removes those three items from storage.

“I jumped straight to a sorted structure which was the right instinct, but I fumbled explaining why removal had to happen atomically and how I'd find the triplet efficiently.”

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Google·Data Scientist·Technical Phone Screen

May 2026
Google DS interview focused on a pretty deep ML question about logistic regression behavior in high-dimensional ad prediction settings. One question, but it had a lot of surface area and I don't think I covered it as cleanly as I should have.
  • For a large-scale binary classifier with hundreds or thousands of features predicting display ad performance, does logistic regression tend to underfit or overfit? Walk through the conditions driving each, how you'd detect it, and what you'd do about it.

“I jumped straight to overfitting because 'high-dimensional' feels like a trap toward regularization talk, but the interviewer pushed back and asked about underfitting too.”

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Google·Software Engineer·Technical Phone Screen

May 2026
Google SWE coding round, one question the whole time. Sliding window stuff, which I thought I knew until I was actually in the hot seat.
  • Given an integer array and a value k, find the length of the shortest contiguous subarray that contains at least k distinct integers. Return -1 if no such subarray exists.

“My first instinct was brute force and I kind of just said it out loud before thinking, which wasn't a great look.”

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