Google interview notes, page 47 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·Onsite - Coding / Algorithms

Apr 2026

Google SWE onsite coding round, early career level. The main problem was a file system traversal question with a few follow-ups that got progressively trickier. Not a bad experience but the follow-ups definitely pushed past what I'd prepared for.

Given the root node of a file system where each node has a class, size, and contains field, compute the total size of all files in the tree.
What is the time complexity of your traversal, and how would you speed up repeated size queries on the same directory?
If a file is added or removed from the file system, how do you efficiently invalidate the cache?
Can you rewrite your recursive DFS solution using an explicit stack instead?

“The base case is pretty clean, just a DFS summing sizes at file nodes.”

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Google·Software Engineer·Onsite - Behavioral / Leadership

Apr 2026

Behavioral round at Google for a software engineering role. Just one question but it had a lot of moving parts and I felt like I was rambling by the end.

What would you do if someone on your team is underperforming? Walk through how you'd figure out what's causing it, give feedback, set expectations, and decide whether to coach them or escalate to formal performance management.

“I had a real example ready but I spent too long on the diagnosis part and then rushed through the feedback and escalation piece.”

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Google·Data Scientist·Technical Phone Screen

Apr 2026

Data scientist interview at Google with two fairly meaty experimentation questions. Both were product-analytics flavored with enough statistical nuance to trip you up if you're not careful about the details.

Your PM wants a two-week A/B test to evaluate raising subscription prices. How would you design it, should you even A/B test pricing at all, and how would you decide whether the new price is actually better?
A sign-up page has a red button at the top. The team wants to test red vs blue and top vs bottom placement to improve click-through. How do you structure and analyze that multivariate test?

“Two weeks is almost certainly not enough and I said so upfront, which felt risky but I think was the right call.”

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Google·Machine Learning Engineer·Technical Phone Screen

Apr 2026

Google ML Engineer interview with a graph traversal problem that had a decent follow-up about scaling. The core question was straightforward if you've done BFS/DFS before, but the optimization discussion is where it got interesting.

You have a set of unordered coordinate points, a start point, an end point, and a function getDistance(x, y). Two points are directly connected if their distance is less than r. Can the start point reach the end point through a chain of such hops?
Follow-up: the point set is now huge and computing all pairwise distances is too expensive. How do you handle this?

“Pretty much a connectivity problem once you see it.”

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Google·Software Engineer·Onsite - System Design / Architecture

Apr 2026

System design round at Google for a software engineering role. The whole session was focused on one big question about building an A/B testing platform from scratch, which sounds scoped until you realize how many moving parts they actually want you to cover.

Design an A/B testing platform that handles experiment definition, user randomization, exposure logging, metric computation, and guardrails. Walk through bucketing strategy, unit consistency, incremental rollout, bias avoidance, and how you'd approach statistical analysis and diagnostics.

“This question is deceptively wide.”

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Google·Software Engineer·Onsite - Coding / Algorithms

Apr 2026

Google SWE coding round, one problem, classic puzzle-style. The question was trickier than it looked on the surface and I spent way too long second-guessing an observation that turned out to be the whole key.

You have a 1-D board represented as a string of 'L', 'R', and '.' characters. 'L' pieces can only slide left into empty spaces, 'R' pieces can only slide right into empty spaces, and pieces can never cross each other. Given a start configuration and a target configuration of the same length, determine if the start can be legally transformed into the target.

“I kept trying to simulate the actual moves, which was a dead end.”

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Google·Software Engineer·Technical Phone Screen

Apr 2026

Google SWE coding round, one meaty design-and-implement question about a restaurant waitlist that ate up most of the session. The follow-up extension changed the whole problem and I had to think on my feet.

Design and implement a restaurant waitlist system supporting two operations: add(party_size, arrival_time) to enqueue a party, and allocate(table_size) to seat and remove the earliest-arriving party whose size exactly matches the given table size.
Extend the allocate operation so that a table of a given size can seat the earliest-arriving party whose size is less than or equal to the table size, not just an exact match.

“My first instinct was to just throw everything in a list and scan linearly, which would have been fine to say out loud but felt embarrassing to lead with at Google.”

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Google·Product Manager·Onsite - Product Sense / Strategy

Apr 2026

Google PM interview with a single product design question about fitting a studio apartment for three people. Short session, but the question had more depth to it than I expected going in.

How would you design a studio apartment intended for three people to live in?

“I went straight into space optimization and started rattling off furniture ideas, which was probably the wrong move.”

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Google·Software Engineer·Technical Phone Screen

Apr 2026

Google SWE interview, coding round focused on graph traversal. One question, no implementation needed, just walk through the approach and complexity. Pretty straightforward if you've done any BFS prep.

You have an N×N grid where 1 is water and 0 is land. Given a start cell and a target cell, find the shortest path between them moving only through land cells in 4 directions. Describe your approach and analyze time and space complexity. No code required.

“Classic BFS setup.”

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Google·Product Manager·Onsite - Product Sense / Strategy

Apr 2026

Google PM interview with a single product sense question about improving Maps for group travel. Pretty open-ended, which I wasn't fully ready for.

How would you improve Google Maps specifically for group travel use cases?

“I went straight to features without really anchoring on who the user was first.”

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