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Google interview notes, page 151 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·Technical Program Manager·Onsite - Behavioral / Leadership

Jun 2026
Google TPM interview with a scenario about handling executive skepticism on first contact. Just one question but it's the kind that separates people who've actually dealt with difficult stakeholders from people who've only read about it.
  • You're meeting a customer executive for the first time and they immediately question your credentials. How do you handle it?

“My first instinct was to defend myself and I almost went down that road.”

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Google·Software Engineer·Onsite - Coding / Algorithms

Jun 2026
Three algorithmic problems back to back at Google for a software engineer role. Each one had a naive solution they wanted you to name first and then an optimized version, which sounds straightforward but the pressure of doing complexity analysis out loud while coding is its own skill.
  • Given a log of chat messages where each entry is a user ID, find the top K most active users by message count. Discuss at least two approaches and their time and space complexities.
  • Count the number of distinct paths in an m by n grid from the bottom-left cell to the bottom-right cell, where from any cell you can only move to the next column either straight right, diagonally up-right, or diagonally down-right. First give an O(m*n) DP solution, then optimize space to O(m).
  • Given a list of car rental bookings each with a pickup and return time, compute the minimum number of cars needed and produce a valid assignment of rentals to cars. Discuss a naive approach and an optimized one using a priority queue.

“Started with the sort-everything approach which is O(n log n) and they nodded but clearly wanted the heap version.”

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Google·Software Engineer·Onsite - Behavioral / Leadership

Jun 2026
Behavioral round at Google for a software engineering role. Just one question but it stuck with me more than the technical stuff did.
  • If one person on a four-person team is taking more credit than they deserve, how do you handle it?

“I went straight to 'I'd have a direct conversation with them' and the interviewer just kind of waited, like that wasn't enough.”

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Google·Software Engineer·Onsite - Behavioral / Leadership

Jun 2026
Behavioral round at Google for a SWE role. Just the one question but they really sat with my answer and pushed on it.
  • Tell me about a time you had to work with a difficult person at work.

“I had a story ready but halfway through telling it I realized it made me sound just as difficult as the other person.”

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Google·Software Engineer·Technical Phone Screen

Jun 2026
Phone screen coding round at Google for a SWE role. The base problem was easy but it's basically just the warm-up; the real graded part is a parallel search follow-up that catches a lot of people off guard if they haven't practiced it.
  • Implement a binary search to find the first bad version given a monotonic isBadVersion(v) oracle over versions 1 to n.
  • Now parallelize that search: partition the version range into buckets, probe the bucket boundaries concurrently to find which bucket contains the good-to-bad transition, then binary search within that bucket. Write working code.

“Straightforward binary search, nothing tricky.”

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Google·Data Scientist·Technical Phone Screen

Jun 2026
Ran into a probability/stats question at Google that felt straightforward until I second-guessed myself halfway through. Short interview, one question, left not totally sure how I did.
  • If you subtract one normally distributed variable from another, where both are independent and identically distributed with mean 1 and variance 3, what distribution does the result follow?

“I knew the answer involved a normal distribution but fumbled the parameters for a second.”

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Google·Software Engineer·Technical Phone Screen

Jun 2026
Graph algorithms question at Google for a SWE role. Pretty classic Dijkstra territory but with the twist of counting paths, not just finding the shortest distance. No frills, just code.
  • Given a weighted undirected graph with non-negative edge weights, count the total number of shortest paths between a source node and a target node. If the target is unreachable, return 0.

“The distance part clicked fast for me since it's basically Dijkstra.”

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Google·Machine Learning Engineer·Technical Phone Screen

Jun 2026
Google ML Engineer interview with a probability/dynamic programming question that felt more like a math puzzle than anything ML-specific. Clean problem statement but the edge cases will get you if you're not careful.
  • You start at position 0 on an infinite 1D number line. Each turn you roll a fair K-sided die and move forward by the result. You win if you land anywhere in the range [mn, mx], and lose if you overshoot past mx without ever hitting that range. Given K, mn, and mx, compute the probability of winning.

“Took me a minute to see this as a DP problem.”

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Google·Product Manager·Onsite - Product Sense / Strategy

Jun 2026
Google PM interview with a pretty out-there product design question. Felt like they wanted to see how you handle a weird constraint rather than whether your answer was 'right'.
  • Design a product that helps flightless birds navigate a large city.

“I stared at this for a second and genuinely wasn't sure if it was a joke.”

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Google·Software Engineer·Technical Phone Screen

Jun 2026
Google SWE interview with a game simulation problem. The problem itself was pretty well-defined but the state space and move logic had enough edge cases to keep things interesting for a while.
  • Implement a two-player game called Babylon. There are 12 tiles in 4 colors (3 of each), each starting as a stack of height 1. On each turn, a player merges two stacks, which is only legal if the stacks share the same height or the same top color. The merged stack's height is the sum of both, and the top color is whichever stack was placed on top. The player who makes the last legal move wins. Given the initial arrangement of tiles, simulate or analyze the game.

“I spent probably the first five minutes just re-reading the rules because the win condition tripped me up.”

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