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Google interview notes, page 69 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

May 2026
Google MLE coding round focused on a web crawler problem. The interviewer was a long-tenured Googler who clearly cared a lot about Python internals, and I was not prepared for that angle at all.
  • Implement a web crawler using both DFS and BFS approaches.
  • How does Python handle instance behavior and what are the implications for this kind of recursive solution?
  • What are Python's recursion and stack depth limits, and how would they affect a DFS-based crawler?
  • What kinds of hash-related bugs could appear in a web crawler implementation?

“Got both solutions down, which felt fine in the moment.”

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Google·Data Scientist·Technical Phone Screen

May 2026
A Google data scientist round that was basically one long case question about diagnosing a dual-market usage drop. Dense and technical, covered everything from causal inference to stakeholder communication.
  • Usage is down 10% in the US and 11% in Mexico. What confounders would you consider before drawing any conclusions?
  • Design an experiment to figure out whether something you changed actually caused the drop. Walk through your randomization unit, exposure design, primary metric, guardrails, duration, statistical power, and how you'd pre-register it.
  • If a randomized experiment isn't feasible here, how would you use difference-in-differences or a synthetic control instead? What are the identification assumptions and how would you test them?
  • Which covariates would you stratify on or control for, and how would you slice the results once you had them?
  • How would you present these findings and confidence intervals to non-technical stakeholders, and what remediation options would you put on the table?

“I rattled off the obvious ones first: seasonality, competitor moves, maybe a pricing change.”

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Google·Software Engineer·Technical Phone Screen

May 2026
Google SWE interview with a surprisingly tricky combinatorial game theory problem dressed up as a tile-stacking puzzle. The problem had a lot of moving parts and figuring out what even constitutes a valid state took a while.
  • You have 12 stacks of tiles, each starting at height 1 with one of 4 colors (3 tiles per color). Two players alternate turns, and each turn a player must merge one full stack onto another. A merge is legal if the two stacks share the same height or the same top color. The merged stack's height is the sum of both, and the top color comes from whichever stack was placed on top. The player who can't move loses. Implement the game, determine the winner from a starting configuration, or build the move logic.

“The rules sound manageable until you start implementing and realize the state space is genuinely large.”

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Google·Data Scientist·Technical Phone Screen

May 2026
Google data scientist interview with a heavy stats focus. The whole session basically revolved around one meaty confidence interval question with several sub-parts, which felt more like a stats oral exam than a typical DS interview.
  • You have a sample of n=100 with a mean of 100 and a 95% CI of ±10. If you increase n to 10,000 while keeping the sample standard deviation the same, what is the new CI half-width and why does it scale the way it does?
  • Without increasing sample size, what are at least four ways you could narrow the confidence interval, and what are the trade-offs of each?
  • Under what conditions is narrowing the CI by simply raising alpha misleading, and how would you explain that risk to someone without a statistics background?

“The math itself is straightforward once you remember the half-width is z times s over root n.”

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Google·Product Manager·Onsite - Product Sense / Strategy

May 2026
Google PM interview with a classic operations/product scaling question. Nothing too fancy but it required thinking on your feet about tradeoffs between user experience and logistics at scale.
  • If a grocery store's foot traffic suddenly jumped to 10x its normal volume, how would you redesign the store layout and operations to handle it?

“I went straight to the physical layout changes and kind of forgot to anchor on who the user even is in this scenario.”

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Google·Product Manager·Onsite - Product Sense / Strategy

May 2026
Got a classic estimation question for a Google Maps-adjacent role. Pretty standard case interview format but I fumbled the structure more than I expected.
  • Estimate the cost for Google Maps to collect Street View imagery across all of Manhattan.

“I jumped straight into breaking down the geography before thinking about what actually drives cost.”

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Google·Software Engineer·Onsite - Product Sense / Strategy

May 2026
This was a Google software engineer interview that went deep into career strategy and decision-making under immigration constraints, not the usual leetcode grind. The main question was essentially a multi-variable optimization problem about what to do after a layoff, with a green card priority date adding real pressure to every option.
  • You've been laid off and have seven possible paths forward (including returning to the US on H-1B, working in China or Hong Kong or Singapore, pursuing a Canadian master's or a PhD, finding H-1B sponsorship, or switching to F-1 with Day-1 CPT). Your employment-based green card priority date is May 2023 and you're worried it could become current while you're studying. How do you decide what to do? Walk through your decision criteria, what information you'd gather, a weighted decision matrix, your recommendation, and a 90-day action plan for your top two options with pivot triggers.

“This one is genuinely hard to prepare for because it's not a system design or an algo question, it's basically a life decision framed as a case study.”

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Google·Software Engineer·Onsite - Coding / Algorithms

May 2026
Google SWE coding round with a board simulation problem. Not the hardest thing I've seen but the edge cases tripped me up more than I'd like to admit.
  • Given a one-dimensional board with pieces and coins, find the maximum number of coins that can be collected by moving pieces left-to-right, where a piece collects a coin by landing on it and no two pieces can occupy the same cell.

“My first instinct was greedy and I think that's mostly right, just match each piece to the nearest coin to its right without overlapping assignments.”

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Google·Product Manager·Onsite - Product Sense / Strategy

May 2026
PM interview at Google, one question focused on product metrics for a hardware product. Pretty standard product sense territory but the wireless headset angle made it a bit more specific than the usual 'pick a metric' prompts.
  • What metrics would you track if the goal is to grow sales of Google's wireless headset?

“I went straight to revenue and unit sales which felt obvious in hindsight.”

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Google·Product Manager·Onsite - Behavioral / Leadership

May 2026
Behavioral round at Google for a PM role, pretty much one question that took up most of the time. Standard stakeholder alignment territory but they pushed hard on specifics.
  • Describe a situation where you had conflicting priorities across different stakeholders and walk through how you got everyone aligned.

“I had a decent story ready but halfway through I realized I was spending too long on the setup and not enough on what I actually did to resolve it.”

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