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Google interview notes, page 41 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 - Product Sense / Strategy

Apr 2026
Interviewed for a product designer role at Google, got asked about user research methods. Pretty standard stuff but still worth thinking through how you'd frame it.
  • Walk me through how you approach user research.

“I talked about generative vs evaluative research and when I'd pick one over the other.”

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Google·Product Manager·Onsite - Product Sense / Strategy

Apr 2026
Google PM interview with a classic product sense question. Pretty standard setup but the open-endedness of it made me second-guess my answer the whole way through.
  • Pick a non-technical product you use regularly. How would you improve it?

“I went with a physical product thinking it'd be easier to reason about, which in hindsight was a mistake because I spent too long on the problem space and not enough actually proposing anything concrete.”

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Google·Software Engineer·Onsite - System Design / Architecture

Apr 2026
System design round at Google for an EM role. The question was a full-blown photo/video storage system at Google Photos scale, and it went pretty deep into storage architecture, ML pipelines, and cost tradeoffs. Left feeling like I covered maybe 70% of what they were looking for.
  • Design a photo and video storage service similar to Google Photos that can handle billions of users and hundreds of billions of media files, including upload/download, browsing by date/album/face/location, sharing, and cross-device sync.

“I started with object storage and a metadata layer which felt right, but I spent way too long on the upload flow and didn't get to the ML feature extraction pipeline until they nudged me.”

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Google·Software Engineer·Technical Phone Screen

Apr 2026
Google ML round for a software engineering role, split between a resume walkthrough and a fairly deep dive into LLM and generative AI fundamentals. The technical portion leaned heavily on knowing the tradeoffs between fine-tuning approaches and understanding generative architectures at a conceptual level, not just name-dropping them.
  • Walk me through your resume and relevant experience.
  • What are the main LLM fine-tuning methods, and how do you decide which one to use for a given situation?
  • You're fine-tuning a model on a small dataset and notice performance on the original task has degraded. How do you approach this?
  • How much data do you actually need to fine-tune an LLM effectively, and what factors influence that number?
  • Compare autoencoders, variational autoencoders, and VQ-VAEs. What are the differences in their objectives and latent representations?

“Standard opener, took about ten minutes.”

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Google·Product Manager·Onsite - Behavioral / Leadership

Apr 2026
Interviewed for a PM role at Google and got asked one of those culture-fit questions that sounds easy but kind of isn't.
  • How would you describe your ideal work environment and the kind of colleagues you work best with?

“I rambled.”

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Google·Product Manager·Onsite - Product Sense / Strategy

Apr 2026
Google PM interview with a classic product design question set in the future. Pretty open-ended, which I wasn't fully prepared for.
  • Design a teleportation device for the year 2050.

“I spent the first minute just kind of staring at the prompt internally.”

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Google·Software Engineer·Onsite - System Design / Architecture

Apr 2026
Google system design round for a software engineering role. The question was one big pipeline design problem and they just let it run for the full hour, which I wasn't totally ready for.
  • Design a data pipeline that reads many text files of varying sizes and produces output files of exactly 100 MB each. The output must preserve line boundaries, include every input line exactly once, and handle concerns like partial lines at chunk boundaries, buffering strategy, compression, parallelism, and fault tolerance with exactly-once semantics.

“This wrecked me a little.”

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Google·Machine Learning Engineer·Technical Phone Screen

Apr 2026
Interviewed for an ML engineer role at Google and got hit with a conceptual question about CNNs vs RNNs. Pretty standard for this kind of role but still made me second-guess how deep to go.
  • What are the key differences between Convolutional Neural Networks and Recurrent Neural Networks?

“I went with the spatial vs sequential angle, CNNs are built for grid-like data where local patterns matter, RNNs are for sequences where order and context carry meaning.”

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Google·Software Engineer·Technical Phone Screen

Apr 2026
Google SWE interview with a bit-manipulation flavored DP problem. The follow-up on the greedy rule for odd numbers is where things got interesting and also where I felt least confident.
  • Given a positive integer n, find the minimum number of operations to reduce it to 1. Each operation lets you divide by 2 if n is even, or increment/decrement by 1 if n is odd. What's the optimal greedy strategy for odd numbers, and how do you derive it from the last two bits?

“I got the basic idea fast enough: halve when even, and for odd just pick whichever of n+1 or n-1 gets you to a better state faster.”

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Google·Product Manager·Onsite - Product Sense / Strategy

Apr 2026
Google PM interview, product sense round. One question about a household device, which sounds easy until you realize how many directions you can go with it.
  • You're a PM for a household device. What three features would you recommend adding, and why?

“I spent way too long trying to pick the 'right' device in my head instead of just picking one and committing.”

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