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Google interview notes, page 63 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

May 2026
Behavioral screen at Google, just the one question about disagreeing with a manager. Pretty standard but it's the kind of thing that's easy to fumble if you haven't thought it through.
  • Describe a situation where your manager had a strong conviction about something you personally disagreed with. What did you do?

“I had a real story for this but I think I spent too long on the setup and not enough on what I actually did.”

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Google·Software Engineer·Technical Phone Screen

May 2026
Google coding screen for a software engineer role, one question about string decompression with nested groups. Pretty focused session, no fluff.
  • Implement a string decompression function where substrings wrapped in parentheses and followed by a repeat count in curly braces get expanded that many times. For example, 'a(abc){3}' becomes 'aabcabcabc', and nesting is supported so 'a(b(c){2}){3}d' becomes 'abccbccbccd'.

“The flat case I got pretty quickly, just scan for the pattern and replace.”

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Google·Software Engineer·Onsite - Behavioral / Leadership

May 2026
Went through a behavioral round at Google for a SWE role. Just the one question but it took up the whole conversation.
  • Tell me about a time you had a conflict with someone. How did you resolve it and what did you learn from it?

“Classic behavioral question but I still fumbled it a bit.”

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Google·Data Scientist·Technical Phone Screen

May 2026
Google DS interview focused entirely on experiment design for a new Maps feature. One long, layered question that kept branching into sub-problems. Felt like a product sense question wearing a stats costume.
  • Google Maps wants to recommend personalized jogging routes. How would you design an A/B test for this feature, including your primary metric, secondary metrics, eligibility and trigger conditions, experimental unit, contamination prevention, guardrails, sample size calculation, and rollout plan?

“This question just kept going.”

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Google·Software Engineer·Technical Phone Screen

May 2026
Interviewed at Google for a software engineering role, got a concurrency question that sounds deceptively simple but has a lot of nuance underneath.
  • When would you choose volatile over synchronized in Java?

“I knew the textbook answer but fumbled explaining the actual tradeoff under pressure.”

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Google·Product Manager·Onsite - Product Sense / Strategy

May 2026
Google product management interview with a classic estimation question about YouTube storage costs. Nothing too surprising but it requires you to actually know how to structure a back-of-envelope calculation under pressure.
  • Estimate the incremental cost of storing all videos uploaded to YouTube over the course of a single year.

“I started by estimating daily upload volume, which felt okay, but then I got tangled up trying to figure out storage cost per GB and whether to account for multiple resolution copies.”

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Google·Software Engineer·Technical Phone Screen

May 2026
Google SWE coding round with a combinatorics-flavored problem that looks deceptively simple until you actually try to implement it efficiently. Not a lot of context shared about how it went, but the problem itself is worth thinking through carefully.
  • Given a list of distinct objects with non-negative weights, find the k subsets whose total weights are the largest, and return those sums in decreasing order.

“My first instinct was to enumerate all subsets and sort them, which works but blows up for any reasonably sized input.”

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Google·Product Manager·Onsite - Product Sense / Strategy

May 2026
Went through a product sense round at Google, basically just one big design question about an existing Google product. Pretty standard for this type of role but I underestimated how much they'd push on execution details.
  • How would you improve Google Translate?

“I jumped straight into features, which was a mistake.”

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Google·Software Engineer·Onsite - Coding / Algorithms

May 2026
Google onsite coding round for a new grad SWE position. One problem the whole session, but it had enough edge cases to keep things interesting for longer than I expected.
  • Given a string and a max line width, implement a word-wrap function that returns the number of lines needed to render the text, correctly handling explicit newline characters and consecutive whitespace.

“Framed as 'imagine a Google Doc page with a fixed character width.' Sounds straightforward until you realize how many edge cases pile up: what if there's a newline right after another newline (empty line still counts), what if the input starts with whitespace, what if a word lands exactly at the width limit.”

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Google·Machine Learning Engineer·Onsite - Behavioral / Leadership

May 2026
Behavioral round for an ML engineer role at Google. Two questions, both the classic conflict and failure prompts. Nothing surprising about the topics but I underestimated how much they'd push on specifics.
  • Tell me about a time you had a significant conflict with someone at work, such as a disagreement with a teammate, PM, or manager. How did you handle it?
  • Describe a significant failure you experienced, such as a missed deadline, a project that fell short of its goals, or a production incident. What happened and what did you take away from it?

“I went in thinking I had a solid story about a disagreement with a PM over model complexity versus shipping speed.”

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