Google interview notes, page 192 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·Onsite - Coding / Algorithms

Jun 2026

Google ML Engineer interview, got a coding round with a data structures problem that had a twist I didn't fully see coming. The LRU variant with pinning caught me off guard in the implementation details even if the concept felt familiar.

Design and implement a modified LRU cache that supports get, put, pin, and unpin operations, where pinned keys cannot be evicted until explicitly unpinned. All operations should run in average O(1) time.

“I knew LRU cold going in, doubly linked list plus a hashmap, done.”

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Google·Software Engineer·Onsite - Multi Round

Jun 2026

Went through the full Google SWE onsite and felt pretty solid coming out of it, two medium coding problems, one graph, one stack, both solved optimally. Behavioral and communication were flagged as strong. Still got rejected by the hiring committee despite both interviewers thinking I'd pass, which honestly just stings in a way that's hard to put into words.

Graph traversal medium-difficulty coding problem.
Stack-based medium-difficulty coding problem.

“Gave the optimal solution, no issues I can recall.”

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Google·Product Manager·Onsite - Product Sense / Strategy

Jun 2026

Interviewed at Google, got hit with a product sense question that felt deceptively casual but had some real depth to it once I started talking.

How would you explain Twitter to an elderly person?

“I started with the obvious analogy, like a public bulletin board, and it felt okay but kind of flat.”

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Google·Product Manager·Onsite - Product Sense / Strategy

Jun 2026

Google PM interview with a classic hardware-meets-constraints product design question. Short session, one question, but it took more mental energy than I expected.

How would you design a coffee machine intended for use on the International Space Station?

“I went straight to user needs and completely forgot to address the physical constraints for like two full minutes.”

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Google·Product Manager·Onsite - System Design / Architecture

Jun 2026

Interviewed for a PM role at Google and got a technical question I wasn't expecting to see in that context.

What is the best approach for connecting SQL databases, and what is your reasoning?

“Wasn't sure if they wanted a PM-level answer or something more technical.”

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Google·Product Manager·Onsite - Product Sense / Strategy

Jun 2026

Google PM interview, one question on monetizing a smoking cessation app called Quitsure. Pretty lean on details but it's a solid product strategy case.

How would you monetize the Quitsure app?

“I went straight to freemium and subscription tiers, which felt obvious in retrospect.”

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Google·Data Scientist·Onsite - System Design / Architecture

Jun 2026

A Google data scientist interview that was basically one massive ML system design question covering the full lifecycle of a purchase prediction model. Dense, technical, and they clearly wanted to see if you could hold all the moving parts in your head at once.

Design an end-to-end pipeline to predict (1) whether a user will spend anything in the next 7 days and (2) how much they'll spend, using event and order data up to a fixed cutoff date.
How do you prevent data leakage given a training cutoff of 2025-08-31, and what are two concrete features that seem predictive but are actually leaky, and how would you fix them?
Walk through your time-based cross-validation strategy for this problem. Why not use standard k-fold?
How would you handle class imbalance in the classification task, and what metrics would you use for each of the two tasks?
Given an asymmetric cost matrix where false negatives are more expensive than false positives, how do you pick a classification threshold?
How would you detect and fix segment-specific regressions after model deployment, for example if the model degrades for a particular user cohort?
Design an offline and online evaluation plan for this model, including rollout strategy and holdback groups.
How would you set up post-deployment monitoring for this model, specifically around feature drift, label delay, and model decay?

“This is a two-headed model problem and I kept wanting to collapse it into one, which was the wrong instinct.”

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Google·Product Manager·Onsite - Product Sense / Strategy

Jun 2026

Google PM interview with a single product design question asking you to build something to go up against Spotify. Pretty open-ended, which sounds fun until you're actually in it.

Design a competitor product to Spotify.

“I went straight into features and had to course-correct fast when I realized I hadn't defined who I was building for.”

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Google·Software Engineer·Onsite - Coding / Algorithms

Jul 2026

Google SWE coding round, one problem the whole session. Converting RPN to infix with minimal parentheses. Sounds manageable until you actually sit down and think through all the precedence and associativity edge cases.

Given a Reverse Polish Notation expression as a comma-separated string, convert it to an equivalent infix expression using the minimum number of parentheses necessary to preserve correctness.

“The stack-based structure clicked pretty fast for me.”

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

Jul 2026

Did a system design round at Google for an MLE role. The question was around building a recommendation system and my feedback came back pretty lukewarm, so make of that what you will.

Design a recommendation system for products or videos.

“Felt okay in the moment but the feedback said otherwise.”

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