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Google interview notes, page 177 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·Machine Learning Engineer·Technical Phone Screen

Jun 2026
One question from what looks like a Google ML engineer interview, asking about trends and challenges in the space. Not much context to go on but here's what I've got.
  • What are the current trends and challenges in your area of ML specialization?

“Broad question that sounds easy until you're actually sitting there trying to not sound like you just read a Medium article.”

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Google·Machine Learning Engineer·Onsite - System Design / Architecture

Jun 2026
Google system design interview for what felt like an ML-adjacent role. One question, image classification with some real-world constraints baked in. Not a pure coding round but not a pure ML theory round either, somewhere in the middle.
  • Design a system that can automatically identify images of bald eagles captured in national parks.

“I started with data collection and spent way too long on it.”

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Google·Software Engineer·Onsite - Product Sense / Strategy

Jun 2026
Google product design interview with a single accessibility-focused design prompt. Pretty open-ended, which sounds freeing until you're actually sitting there trying to figure out where to start.
  • Design a cooking apparatus for someone who is blind.

“I spent the first couple minutes thinking about kitchen gadgets when I should've been asking clarifying questions about the user.”

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Google·Product Manager·Onsite - Product Sense / Strategy

Jun 2026
Got a product analytics question framed around Netflix but apparently this was a Google interview. One question, pretty open-ended, and I'm still not sure I nailed the structure.
  • At Netflix, roughly 1 million users are dropping off around the 6-month mark after signing up. What might be causing this, and what would you do about it?

“I went straight into hypotheses without segmenting the user base first, which I regretted about two minutes in.”

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Google·Software Engineer·Onsite - Coding / Algorithms

Jun 2026
Google SWE coding round, one problem the whole session. The question was a scheduling/heap problem and the interviewer wanted both a working solution and a complexity discussion. Not a bad experience but definitely not easy.
  • Given n rooms and a list of meetings with start and end times, assign each meeting to the lowest-index available room. If no room is free, the meeting waits until one opens and keeps its original duration. Return the room index that hosted the most meetings, breaking ties by lowest index. Walk through a two-heap solution and analyze correctness plus time and space complexity.

“I knew heaps were the right move pretty quickly but fumbled on the tie-breaking part.”

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Google·Software Engineer·Technical Phone Screen

Jun 2026
Google SWE interview with a meaty graph validation problem that covered both correctness checking and cycle detection. More algorithmic depth than I expected for a single question.
  • Design a function that validates an e-learning course catalog. Given a set of course IDs and a list of prerequisite pairs (u, v) meaning v requires u, determine whether the catalog is valid: no prerequisite references a course that doesn't exist, and there are no cycles in the dependency graph. If valid, return true and a valid course ordering. If not, return false along with any missing course references and at least one detected cycle. Describe your algorithm and its time and space complexity.

“This is basically two problems stapled together and I didn't see that fast enough.”

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Google·Product Manager·Onsite - Cross-functional / Panel

Jun 2026
Google PM interview, one question about cross-functional dependencies and what you do when another team is slowing you down. Pretty standard for the role but it has more depth than it looks.
  • A cross-functional team your feature depends on isn't moving at the pace you need. How do you handle it?

“I went straight to escalation in my answer and could tell that wasn't the right move.”

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Google·Data Scientist·Onsite - Behavioral / Leadership

Jun 2026
Behavioral round at Google for a data scientist role. The whole session seemed to revolve around one big cross-functional collaboration question, structured around situation, actions, and results. Not a lot of technical depth, but don't let that fool you into under-preparing.
  • Walk me through a time you worked with a cross-functional team (including people from product, engineering, design, or legal) to deliver a data science project. How did you align everyone, handle disagreements, and what was the measurable outcome?

“This is deceptively hard to answer well.”

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Google·Product Manager·Onsite - Product Sense / Strategy

Jun 2026
Google interview for what seemed like a product or hardware-adjacent role. One question, pretty open-ended, and I left unsure if I actually answered it well or just talked in circles for ten minutes.
  • How would you go about launching a newly developed drone product?

“I jumped straight into distribution channels and kind of forgot to anchor on who the customer even was.”

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Google·Software Engineer·Technical Phone Screen

Jun 2026
Google SWE technical phone screen, one probability/DP problem the whole time. The interviewer pushed hard on the analytical approach and made it clear simulation wasn't going to cut it.
  • You start at position 0 on an infinite 1D grid. Each turn you roll a fair K-sided die and move forward by the result. If you land inside the range [mn, mx] you win; if you go past mx you lose. What's the probability of winning, and can you derive it analytically rather than via simulation?

“My first instinct was Monte Carlo and I basically said so out loud, which was a mistake.”

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