Google interview notes, page 150 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

Jun 2026

Google SWE coding round with a chain-splitting problem that looks deceptively clean until you start thinking about the follow-up. The core question is manageable but the extension tripped me up a bit.

You have an array of link weights representing a golden chain. Remove exactly one link, reconnect the two remaining segments into a single chain, then make one cut to split it into two pieces of equal total weight. Return true if any valid removal index exists, false otherwise. Follow-up: return all valid (removal index, cut position) pairs.

“The base case clicked pretty fast once I realized removing a link and reconnecting is just prefix sums on the modified array.”

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Google·Product Manager·Onsite - Behavioral / Leadership

Jun 2026

Google behavioral round, one big question about navigating ambiguity. Pretty open-ended and I wasn't totally sure what angle they wanted.

Tell me about a time you had to make meaningful progress on a project where the requirements were unclear, data was missing, or goals were in conflict. How did you figure out what to tackle first, keep stakeholders in the loop, and actually get to a result?

“This is the kind of question that sounds manageable until you're actually in it and realize your example is messier than you remembered.”

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Google·Product Manager·Onsite - Product Sense / Strategy

Jun 2026

Got a product strategy question for a PM role at Google, framed around Nest Labs. Single question, pretty open-ended, the kind where you can go a lot of directions and that's kind of the problem.

You're a PM at Nest Labs. How would you build a product specifically targeting millennials?

“I went straight into user segmentation and started rattling off millennial traits like I was reading from a marketing textbook.”

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Google·Software Engineer·Technical Phone Screen

Jun 2026

Went through a Google tech phone screen for a software engineering role. The coding problem itself wasn't too bad but I ran out of time cleaning up the implementation, which has me second-guessing where I stand.

Solve a coding problem with a straightforward base case, then extend your solution to handle a more complex follow-up scenario.

“The base problem was easy and the follow-up wasn't exactly brutal either, but I still ran out of time.”

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Google·Software Engineer·Onsite - System Design / Architecture

Jun 2026

System design round at Google focused entirely on distributed systems debugging. One long, deep question about replica inconsistencies that sprawled into CAP theorem territory by the end. Felt like a reasonable session but I probably undercooked the repair/mitigation side.

A distributed database is showing data inconsistencies across its replicas. Walk through the likely causes, how you'd investigate, what you'd look at in logs and telemetry, how you'd verify consistency guarantees, and how you'd repair the system. Also discuss the trade-offs between consistency, availability, and latency while the incident is ongoing.

“This is a beast of a question and I kind of underestimated how wide it was.”

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Google·Data Scientist·Technical Phone Screen

Jun 2026

Google Data Scientist technical screen, coding-heavy with a focus on NLP fundamentals. The problem started simple enough but the follow-ups pushed into runtime optimization and probability, which is where things got interesting.

Implement a next-word prediction model with a train() function that takes tokenized sentences and a predict() function that returns the most likely word to follow a given input word, based on observed adjacent word pairs.
Your naive predict() runs in O(k) time where k is the number of distinct next words seen after a given word. How would you redesign the training step so prediction is O(1) average time?
Modify predict() to return a next word sampled randomly according to the empirical distribution of observed next words, rather than always returning the most frequent one.

“The base version wasn't too bad.”

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Google·Software Engineer·Hiring Manager Screen

Jun 2026

Hiring manager screen at Google for an EM role. Two questions, pretty standard stuff, nothing that felt like a curveball.

Walk me through your background and what your role looked like on your most recent team.
What is drawing you to this particular role?

“Talked through my last team structure, scope, how many engineers I was managing.”

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Google·Software Engineer·Onsite - System Design / Architecture

Jun 2026

Google system design interview centered around building a matchmaking system for video games. Not a lot of context to go on from what was shared, but the core problem is interesting enough that I wanted to log it somewhere.

Design a matchmaking system for a video game. How would you pair players together fairly and efficiently at scale?

“This is the kind of question where you can go in ten directions and none of them are wrong, which is almost worse than having a clear answer.”

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Google·Software Engineer·Technical Phone Screen

Jun 2026

Prepped for a Google data engineering screen and got a classic big data trade-off question. Not much to report beyond that one question, but it's the kind of thing that trips people up if they haven't thought it through carefully.

In what scenarios would you prefer Hadoop over PySpark for data processing?

“I knew the broad strokes but fumbled the specifics a bit.”

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Google·Software Engineer·Onsite - Coding / Algorithms

Jun 2026

Google SWE coding round with a geometry/interval problem that looks deceptively simple until you're actually in the room trying to explain edge cases about infinity. Pretty standard algorithmic interview but the problem had enough wrinkles to keep me on my toes.

You're given a list of planets on a 1D number line, each defined by a center coordinate and a radius of gravitational influence. Every planet makes an interval unsafe. Find all maximal continuous safe intervals on the real line, including potentially unbounded ones on either end, and return them sorted.

“My first instinct was to just merge the unsafe intervals and return the gaps, which is basically right, but I fumbled the unbounded ends.”

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