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Google interview notes, page 60 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·Technical Phone Screen

May 2026
Google interview, project walkthrough round. Pretty standard stuff but it's easy to underestimate how much they want you to own your narrative rather than just describe what the team did.
  • Walk me through one or two projects that are most relevant to this role, covering the problem, your specific contributions, key technical decisions, challenges you faced, and the impact.

“I picked two projects and immediately regretted it.”

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Google·Technical Program Manager·Hiring Manager Screen

May 2026
Interviewed for a TPM role at Google, one question about program success measurement. Short and focused, felt more like a screen than a deep dive.
  • How would you measure the success of a program?

“I went straight to delivery metrics and kind of forgot to talk about business impact until they nudged me.”

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Google·Machine Learning Engineer·Technical Phone Screen

May 2026
Google ML engineer interview, got a tree problem which felt a bit out of left field for the role but wasn't the worst thing in the world.
  • Write a function to calculate the height of a binary tree.

“Pretty classic recursion problem.”

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Google·Product Manager·Onsite - Product Sense / Strategy

May 2026
Google PM interview with a classic exec communication system design prompt that then got layered with constraints. The follow-up questions about MVP scoping and low adoption were the real test.
  • Design a communication system for a high-profile executive (like a CEO) to connect with international business partners, assuming unlimited resources.
  • Now assume you have 1 year and 20 engineers. What does your MVP look like, and what is your go-to-market strategy?
  • After a year on the market, the platform only has 5 calls per month. What do you do next?

“The unlimited resources framing is a trap I walked right into.”

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Google·Software Engineer·Hiring Manager Screen

May 2026
Google engineering manager screen, just one scenario question about handling a dev who skips testing. Short interview but it made me think harder than I expected.
  • One of your developers isn't testing their work. How do you handle it?

“I jumped straight to 'have a direct conversation' which felt right but also kind of obvious.”

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Google·Product Manager·Onsite - Product Sense / Strategy

May 2026
Google PM interview with a product strategy case focused on Google Glass. Pretty open-ended, which sounds fun until you're actually sitting there trying to justify a market for a product most people associate with awkward tech bros at coffee shops.
  • You're the PM for Google Glass. Identify a new target market and design a full launch strategy for it.

“My first instinct was consumer, which is probably the wrong instinct given how badly that already went.”

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Google·Software Engineer·Technical Phone Screen

May 2026
Google interview question on compression, the kind that sounds like a CS 101 warmup until you're actually in it trying to explain DCT coefficients and entropy coding in the same breath.
  • How do image compression and text compression work, and what are the similarities and differences between them?

“I started with the easy stuff, run-length encoding for text, lossy vs lossless for images, but then kind of trailed off when trying to connect them.”

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Google·Data Scientist·Technical Phone Screen

May 2026
A stats-heavy Google data scientist interview that was basically a probability theory exam. Four parts covering normal distributions, MLE derivations, bivariate conditionals, and bias correction. No behavioral fluff, just math.
  • Write the pdf and cdf of a Normal distribution with mean μ and variance σ², then express P(μ − σ ≤ X₁ ≤ μ + σ) in terms of the standard Normal cdf Φ.
  • Derive the MLEs of μ and σ² under three scenarios: σ² known, both unknown, and μ constrained to be non-negative. For the constrained case, explain what the MLE is when the unconstrained estimate is negative.
  • For a bivariate Normal (X, Y), derive the conditional distribution of X given Y = y. Give the conditional mean, variance, pdf, and cdf, then compute P(X > t | Y = y) using Φ.
  • Is the standard MLE of σ² unbiased? If not, what is an unbiased estimator, and how does it relate to the MLE?

“This felt like a warmup but I fumbled the cdf notation at first, wrote it without the standardization step and had to backtrack.”

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Google·Software Engineer·Onsite - System Design / Architecture

May 2026
Google system design round for an engineering manager role, one question about building a sentence autocomplete system like the kind you'd see in Gmail or Docs. Pretty open-ended, which I wasn't fully prepared for.
  • Design a sentence autocomplete suggestion system. For example, given the input 'I hope you feel', the system should suggest a completion like 'better soon'. Think about how this works in products like Gmail or Google Docs.

“I started with the obvious stuff, n-gram language models, prefix trees, that kind of thing, but pretty quickly realized the scope was way bigger than I'd framed it.”

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Google·Software Engineer·Technical Phone Screen

May 2026
Google phone screen, got hit with a classic estimation question about air traffic over the Bay Area. Short interview, nothing too wild, but I definitely fumbled my structure a bit.
  • How many airplanes are currently in the air over the Bay Area?

“Fermi estimation.”

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