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Google interview notes, page 14 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 - Behavioral / Leadership

Apr 2026
Behavioral round at Google for a software engineer role, just one question but it required a lot of digging into your own past. Not a bad experience, just more introspective than I expected.
  • Describe a past decision you would make differently if you could. Walk through the original context, why you made the choice you did at the time, what you'd change, and what you took away from it.

“I had a story ready but it was too safe.”

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Google·Software Engineer·Technical Phone Screen

Apr 2026
Interviewed for a business analyst role at Google, got a metrics-focused question that seemed straightforward but had more layers than I expected.
  • What KPIs would you track to evaluate the health of a subscription-based business?

“I jumped straight to MRR and churn rate, which felt obvious in hindsight.”

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Google·Machine Learning Engineer·Technical Phone Screen

Apr 2026
Google ML Engineer interview with a scheduling problem that sounds straightforward until you actually have to implement it. Classic graph problem dressed up with a real-world wrapper.
  • Given a directed acyclic graph of tasks where each task has a duration, and M workers running in parallel, find the minimum time needed to complete all tasks while respecting dependencies.

“I knew this was a topological sort situation but the parallel workers part tripped me up for a bit.”

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Google·Software Engineer·Technical Phone Screen

Apr 2026
Google interview with a classic estimation question about Street View coverage. Not a lot of context to go on, but it's the kind of problem that sounds breezy until you're actually in the seat trying to remember how many miles of road San Francisco even has.
  • How many drivers would Google need to achieve full Street View camera coverage of San Francisco?

“Classic Fermi estimation.”

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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 they really sat in it with you, lots of follow-ups about the specifics of how alignment actually happened.
  • Tell me about a time you worked with people outside your immediate team to get something done. How did you get everyone on the same page, deal with conflicting priorities, communicate across boundaries, and work through disagreements? What was the result and what would you change?

“The part I fumbled was the 'what would you do differently' bit.”

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Google·Technical Program Manager·Technical Phone Screen

Apr 2026
Interviewed for a TPM role at Google and got hit with a machine learning fundamentals question I was not expecting.
  • What is a perceptron?

“Caught me off guard for a TPM screen.”

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Google·Software Engineer·Technical Phone Screen

Apr 2026
Google coding interview with a number theory flavored tree problem. The kind of question that looks like a tree problem but is really just asking if you know how binary works.
  • Given a complete binary tree where nodes are indexed implicitly, find the index of a target node in O(log N) time by using the binary representation of indices to navigate left and right from the root.

“I went straight to thinking about tree traversal and almost wrote out a BFS before something clicked.”

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Google·Product Manager·Onsite - Product Sense / Strategy

Apr 2026
Got a product design question at Google that sounds like a joke until you're actually sitting there trying to figure out what they want from you.
  • How would you design the US flag?

“I stared at this for a second thinking it was a warmup.”

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Google·Product Manager·Onsite - Product Sense / Strategy

Apr 2026
Google PM interview with a pretty gnarly product strategy question about building a database business from scratch with VC money. Just the one question from what I can tell, but it had a lot of layers to it.
  • You receive a $100M investment from a venture capital firm to build an OLTP database product. How would you deploy that capital to make the product cost-neutral and generate growth within 5 years?

“This one took me a minute to even parse.”

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Google·Machine Learning Engineer·Technical Phone Screen

Apr 2026
Domain interview for an MLE role at Google. The whole thing was a deep dive into my resume projects, but with a twist: every project had to be mapped to a real Google product and defended at Google scale. More demanding than I expected.
  • Walk me through your resume projects in technical depth. For each one, explain the problem you were solving, the scale it operated at, and the trade-offs you made.
  • For each project on your resume, which Google product or surface would benefit most from that work, and why that one specifically?
  • What would change about your project if it had to run at Google scale? Think about latency requirements, multi-tenant isolation, privacy constraints, and ML serving costs.
  • What concrete metrics would you track to validate that your project is actually delivering value once deployed to a Google product?

“I came in thinking this was just a standard resume walkthrough and got humbled fast.”

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