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Google interview notes, page 54 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·Product Manager·Onsite - Product Sense / Strategy

May 2026
Google PM interview, one question about product lifecycle and when to pull the plug. Pretty strategic, less about metrics frameworks and more about how you'd actually handle the messy human side of shutting something down.
  • How would you identify that a product is underperforming, and what steps would you take to wind it down while managing stakeholder expectations?

“The metrics part I felt okay about, retention curves, engagement drop-off, revenue vs cost ratios.”

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Google·Software Engineer·Onsite - Behavioral / Leadership

May 2026
Behavioral round at Google for a software engineer role. One question, but it had a lot of layers to it and I spent more time on it than I expected.
  • A teammate who tends to underperform tells you that your work pace is too intense and puts pressure on them. How do you handle that pushback? Walk through how you'd assess the situation, have the conversation, decide whether to involve your manager, and figure out whether to adjust or hold your ground.

“This one is sneaky because the instinct is to frame yourself as the hero who just keeps working hard.”

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Google·Software Engineer·Technical Phone Screen

May 2026
Google data analyst interview with a SQL question that felt more like a CS puzzle than anything I expected for the role.
  • Write a SQL query to generate the Fibonacci series.

“I stared at this for a moment because SQL is not where my brain goes for sequence generation.”

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Google·Product Manager·Onsite - Product Sense / Strategy

May 2026
Got a troubleshooting scenario at Google for a PM role, basically a 'nothing changed but things are broken' situation. The question had a lot of moving parts and I think I handled the structure okay but probably went too deep into the weeds on DB stuff when they wanted higher-level thinking.
  • A client says their web app has been intermittently slow during report generation and searches for the past few months, even though no code has changed. Walk through how you'd troubleshoot this: how do you figure out if it's one user or many, what metrics and logs you'd pull, and how you'd prioritize fixes after forming hypotheses.

“I started by scoping the blast radius, is this one user's browser or are multiple people hitting the same slowness.”

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Google·Product Manager·Onsite - Product Sense / Strategy

May 2026
Google PM interview, just the one question from what I can tell. Not much to go on but it's a classic.
  • How would you explain machine learning to a five-year-old?

“I fumbled this more than I expected.”

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Google·Software Engineer·Onsite - Coding / Algorithms

May 2026
Got a Google SWE round that was basically a math/bit-manipulation problem dressed up as a counting question. Not the usual leetcode grind, more like competitive programming with a proof component. Walked out unsure if I'd even set up the approach correctly.
  • Given an array of N integers, define F(i, j) as the XOR of all elements from index i to j. Count the number of triples (x, y, z) with x ≤ y ≤ z such that F(x, y) XOR F(y, z) is strictly greater than F(x, z).

“The problem looks deceptively like a prefix XOR warmup until you realize you're counting triples satisfying a strict inequality.”

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Google·Product Manager·Onsite - Product Sense / Strategy

May 2026
Google PM interview with a strategy case about a premium airline seeing drops in business class bookings. Single question, pretty open-ended, left me second-guessing my structure the whole time.
  • A premium airline is experiencing declining bookings in its business class category. How would you approach diagnosing and addressing this problem?

“I went straight into root cause territory, which in hindsight was probably too fast.”

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Google·Data Scientist·Technical Phone Screen

May 2026
Google interview, one question about explaining linear regression to a non-technical audience. Pretty short interaction, not much else to report.
  • How would you explain linear regression to someone with no technical background?

“I fumbled the opening a bit, started talking about coefficients before catching myself.”

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Google·Software Engineer·Hiring Manager Screen

May 2026
Interviewed for a BA role at Google, one question about cost metrics. Not much to go on but it stuck with me.
  • What metrics would you track to improve operational cost?

“I went straight to unit cost and headcount ratios, which felt safe but probably too surface-level.”

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

May 2026
Google ML Engineer system design round, one meaty question about video deduplication at scale. The whole session was basically a whiteboard deep-dive into embeddings, ANN search, and where you cut corners without killing accuracy. Felt like I held my own but the tradeoffs section got uncomfortable fast.
  • Design a large-scale system to detect and remove near-duplicate short videos, handling millions of uploads per day.
  • How would you handle the tradeoff between embedding dimensionality and search latency in this system?
  • What approximate nearest neighbor strategies would you consider, and what are the tradeoffs between them?
  • How would you ensure the system remains accurate over time as new video styles and encoding formats emerge?

“I started with feature extraction using a vision transformer and that part went fine.”

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