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Google interview notes, page 199 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·Hiring Manager Screen

Jul 2026
Screened for a product designer role at Google, pretty low-key conversation that mostly centered on what the day-to-day actually looks like in the role.
  • Walk me through what your typical day looks like as a product designer.

“Felt easy at first and then I kind of rambled.”

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Google·Product Manager·Hiring Manager Screen

Jul 2026
Went through a PM interview at Google that leaned heavily on leadership and decision-making. Just the one question but it took up most of the conversation.
  • As a manager, how do you handle trade-offs?

“Felt like a broad question until I was actually in it.”

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Google·Product Manager·Onsite - Product Sense / Strategy

Jul 2026
PM interview at Google with a product sense question about quick commerce. Not a lot of context to go on from what I remember but it was a pretty meaty question if you haven't thought about that space before.
  • How do 15-minute rapid delivery apps work as a business and product?

“I'd looked at a few of these apps as a user but never really broken down the operational model.”

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Google·Software Engineer·Technical Phone Screen

Jul 2026
Google SWE screening focused on system design, specifically building an anomaly detection system. Pretty technical for a phone screen, felt more like a compressed onsite design round.
  • Design an anomaly detection system. Walk through the architecture, how you'd identify anomalies in a stream of data, and how the system scales.

“This is a meaty question for a screening round.”

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Google·Technical Program Manager·Technical Phone Screen

Jul 2026
Google TPM interview with a classic maze problem. Not much context around the experience itself but the question was a solid algorithmic one that caught me thinking about edge cases more than I expected.
  • Given a maze, find the total number of unique paths from the source to the destination.

“I went straight to dynamic programming but kept second-guessing whether the problem allowed revisiting cells.”

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Google·Product Manager·Onsite - Product Sense / Strategy

Jul 2026
Got a product strategy question at Google that felt oddly specific for a PM interview. One question, valet parking, no real context given upfront.
  • How would you approach launching a new valet parking service for hotels?

“My first reaction was confusion, like why is Google asking about valet parking.”

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Google·Software Engineer·Technical Phone Screen

Jul 2026
Google SWE coding round, one question about tree/graph traversal dressed up as a chicken family problem. Cute framing but the core is just figuring out if two nodes are connected in a directed graph.
  • Given a dictionary of parent-child relationships between chickens, write a function that determines whether two given chickens share any familial connection.

“The chicken theme threw me for a second and I almost laughed, but it's really just 'are these two nodes connected in a directed graph.' I went with BFS from both nodes and checked for overlap in their ancestor/descendant sets.”

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Google·Data Scientist·Technical Phone Screen

Jul 2026
Brutal technical screen for a DS role at Google. The whole thing was basically one giant search evaluation problem broken into four parts, and I was not as prepared as I thought I was for the statistics depth they expected.
  • You have a search engine handling 100M queries per day with a heavy-tailed query distribution. Design a stratified sampling scheme with Neyman allocation and an importance-weighted estimator to measure overall and long-tail NDCG@10 changes between two rankers, given a budget of 2,000 human judgments per week. Provide the estimator formula and its variance.
  • How would you combine a small set of human-judged query-document pairs with counterfactual evaluation using click logs via IPS or doubly robust estimation? What are the propensity requirements and how would you collect them?
  • Do a back-of-envelope power analysis for detecting a 0.01 absolute difference in NDCG@10 between two rankers at 95% confidence. What variance assumptions do you need to make?
  • Describe an active learning loop to prioritize labeling budget toward long-tail queries and reduce overall annotation cost. How would you monitor distribution drift week over week?

“This is where I spent most of my mental energy and still felt shaky at the end.”

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Google·Software Engineer·Onsite - Behavioral / Leadership

Jul 2026
Behavioral round for a new-grad software engineer role at Google, 35 minutes, six questions covering the usual bases: intro, motivation, product thinking, goals, workload, and weakness. Pretty standard stuff but the product question had a bit more meat to it than I expected.
  • Tell me about yourself.
  • Why Google, and why this team specifically?
  • If you were building a product meant to be used by everyone, how would you approach it?
  • What are your short-term and long-term career goals?
  • How do you handle having too much on your plate at once?
  • What's a weakness you're actively working on?

“Ran a bit long.”

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Google·Product Manager·Onsite - Product Sense / Strategy

Jul 2026
Google PM interview with a product design question that had a pretty specific constraint baked in. Not the most complex prompt on paper but the demographic and regional angle made it trickier than expected.
  • Design a payment system specifically for teenage boys in Italy.

“I spent the first couple minutes just trying to figure out what the real constraints were.”

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