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Google interview notes, page 32 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

Apr 2026
Interviewed for a PM role at Google and got asked about design review processes. Pretty conceptual question but it caught me thinking harder than I expected.
  • How do you know when a design review is complete?

“I fumbled this a bit because my first instinct was to say 'when everyone signs off' which is basically a non-answer.”

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Google·Software Engineer·Technical Phone Screen

Apr 2026
Google SWE interview with a classic backtracking problem. Nothing too surprising but the in-place constraint is the kind of detail that trips you up if you're not paying attention.
  • Write a program that solves a Sudoku puzzle by filling in the empty cells in-place, following the standard rules: each digit 1-9 appears exactly once per row, column, and 3x3 box.

“The core of this is backtracking and I knew that going in, but I fumbled the box index calculation for a bit.”

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Google·Software Engineer·Onsite - Coding / Algorithms

Apr 2026
Went through two technical onsite rounds at Google for an early career campus role. First round left me second-guessing myself, second felt much smoother. Still waiting to hear back and genuinely not sure which way this goes.
  • Solve a coding problem by identifying the appropriate data structure and implementing it correctly.
  • Solve a coding problem involving a non-trivial loop construct, explaining your approach as you go.

“I picked the right data structure and got the code mostly working on my own, which felt good.”

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Google·Product Manager·Onsite - Product Sense / Strategy

Apr 2026
Google PM interview with a classic estimation question about elevators in a skyscraper. Pretty short session from what I can tell, just the one question but it required more structured thinking than I expected going in.
  • How many elevators would a 100-floor building need?

“I jumped straight into a number and then had to backtrack, which felt messy.”

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Google·Data Scientist·Technical Phone Screen

Apr 2026
Google DS interview that was basically one giant case study on vendor selection modeling. The question sprawled across like five different sub-problems and I kept second-guessing whether they wanted me to go deep on one part or stay broad across all of them.
  • You have historical order data and two third-party vendors. Design a decisioning model to choose between them for each incoming order, minimizing expected total cost while keeping SLA attainment at or above 95% and staying within a monthly budget cap. Write out the objective function explicitly, including price, expected late penalties, expected quality failure costs, and stockout costs.
  • What features would you use and what modeling approach would you take for this vendor selection problem? Justify your choice given class imbalance and the fact that the data distribution shifts over time.
  • Historical routing decisions were made by a biased policy, not randomly. How do you handle that selection bias when learning from this data? Walk through how you'd estimate propensities and stabilize the weights.
  • How would you evaluate this model offline before deploying it, and how would you roll it out online safely given vendor capacity limits?
  • How do you handle cold start for a new vendor, and what would you investigate if vendor A looks cheaper in your model but late penalties are actually increasing in production?

“This is where I spent most of my time and still felt like I left stuff on the table.”

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Google·Product Manager·Onsite - Product Sense / Strategy

Apr 2026
Google PM interview with a single product design question. Pretty open-ended, which I wasn't fully prepared for.
  • Design a product to track animals.

“I went straight to wildlife conservation as the use case, which felt safe but maybe too narrow.”

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Google·Software Engineer·Technical Phone Screen

Apr 2026
Google SWE interview with a waitlist/seating system design question. The problem looked like a clean OOP exercise until the constraints hit and you realized a plain list wasn't going to cut it.
  • Design a restaurant waitlist system that supports adding parties, canceling them, seating them by table capacity in arrival order, and querying a party's current position, all with better-than-linear time per operation.

“My first instinct was a plain linked list and I said so out loud, which was fine for addParty and cancelParty but then seatTable came up and I realized scanning the whole list each time was not going to fly for 200k ops.”

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Google·Product Manager·Onsite - Product Sense / Strategy

Apr 2026
Google PM interview, one question about designing an app for museum visitors. Pretty open-ended, which I wasn't fully ready for.
  • Design a mobile app for museum visitors.

“I spent way too long on the problem setup and barely got to actual features before they nudged me along.”

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Google·Product Manager·Onsite - Product Sense / Strategy

Apr 2026
Google PM interview with a pretty spicy strategy question about Android's response to WhatsApp potentially becoming the default communication layer on the OS. Just the one question but it packed a lot in.
  • If WhatsApp became the default communication layer for Android at the OS level, how should Android respond strategically?

“This one messed with my head a little because Google owns Android but also competes with Meta.”

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Google·Software Engineer·Onsite - Multi Round

Apr 2026
Google onsite for a software engineering role, two coding rounds. One heap/hashing question that went okay-ish, and one segment tree problem that the candidate had literally seen the night before and decided not to study. That second one still stings.
  • Design or implement a solution involving a heap and hashing.
  • Solve a segment tree problem (specifically, a range-based tree modification/query problem).

“Went through it well enough but fumbled some edge cases.”

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