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Google interview notes, page 145 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

Jun 2026
Google PM interview with a single big design question that covers basically everything at once. Product, tech, market sizing, and GTM all rolled into one prompt. I felt underprepared for how broad it was.
  • Design a gift-giving service for Google. Walk through the user interface, the technical implementation approach, a go-to-market plan, and estimate the total addressable market.

“This question is four questions in a trench coat.”

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Google·Machine Learning Engineer·Technical Phone Screen

Jun 2026
Interviewed for an ML engineer role at Google and got asked one of those questions that sounds like a joke but really isn't.
  • How would you explain machine learning to your grandmother?

“I fumbled this more than I expected.”

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Google·Software Engineer·Onsite - System Design / Architecture

Jun 2026
Google system design round, got asked to build a timer system. Not the most glamorous problem but it had more depth than I expected once we got into the weeds.
  • Design a timer system.

“Started with the obvious stuff, a simple delay queue, but the interviewer kept pushing on scale.”

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Google·Software Engineer·Technical Phone Screen

Jun 2026
Interviewed for a solutions architect role at Google and got hit with an API concepts question that felt deceptively simple.
  • What does idempotency mean in the context of an API?

“Knew what idempotent meant but stumbled trying to explain it clearly under pressure.”

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Google·Product Manager·Onsite - Product Sense / Strategy

Jun 2026
PM interview at Google with a classic estimation question about bike-sharing in NYC. Pretty standard product sense round, nothing too surprising.
  • How many bikes would you need to launch a bike-sharing service in New York City?

“I started with population and geographic density, broke NYC into boroughs, then tried to estimate daily trips per bike.”

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Google·Product Manager·Hiring Manager Screen

Jun 2026
Interviewed for a PM role at Google, and it was pretty much just one question that set the tone for the whole thing.
  • Why do you want to work at Shure?

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Google·Software Engineer·Onsite - Behavioral / Leadership

Jun 2026
Interviewed for a SWE role at Google. Just one behavioral question about what success means to you, which sounds easy until you're actually sitting there trying to not sound like a LinkedIn post.
  • What does success look like to you, and how are you actively working toward it?

“I rambled.”

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Google·Software Engineer·Onsite - Coding / Algorithms

Jun 2026
Google SWE coding round, one algorithmic question about substrings. Pretty light on details but the problem itself is deceptively tricky if you haven't seen it before.
  • Given a string, return all of its unique substrings.

“Looks easy until you start thinking about duplicates and how to enumerate efficiently.”

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Google·Data Scientist·Technical Phone Screen

Jun 2026
A Google Data Scientist interview that was essentially one massive case question about Google Meet call drops. The whole thing was a structured product analytics exercise but it went pretty deep into experimentation, business impact quantification, and prioritization frameworks all rolled into one.
  • A major enterprise customer is reporting frequent Google Meet call drops. Walk through a complete analysis plan: how would you define a 'drop,' what instrumentation would you need, and how would you handle correlated failures to avoid double-counting?
  • How would you quantify the business impact of call drops across three layers: meeting time lost, user productivity, and account-level retention risk? And how would you estimate the marginal impact of a 1 percentage point increase in the drop rate?
  • How do you decide whether to prioritize fixing this bug versus building a new feature? Walk through the expected value calculation and how you'd factor in opportunity cost and guardrail metrics.
  • How would you determine whether this increase in call drops is a regression? What signals or experiment designs would you use?
  • How would you structure a decision memo for this situation, including your assumptions, a sensitivity analysis, and the specific observations that would invalidate your recommendation?

“The definition piece tripped me up more than I expected.”

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Google·Product Manager·Onsite - Product Sense / Strategy

Jun 2026
Google PM interview, single product sense question about improving ratings for a kids meditation app. Pretty focused case, no behavioral rounds mentioned.
  • You're the PM for a kids meditation app that has a 3 to 5 star average rating on the Play Store. How would you improve that rating?

“The rating range is weirdly wide and I spent too long trying to figure out if that was intentional.”

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