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Google interview notes, page 42 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

Apr 2026
Google interview with a scheduling/availability problem. Pretty classic but the edge cases sneak up on you if you're not careful.
  • Design an algorithm that finds all time slots where multiple people are simultaneously available.

“My first instinct was to just do a brute-force overlap check and I started going down that path before realizing it gets messy fast with more than two people.”

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Google·Product Manager·Onsite - Product Sense / Strategy

Apr 2026
Interviewed at Google, product or engineering adjacent role, got asked a classic estimation question about offline maps storage. Short post, not much context around the round or outcome.
  • How much storage space would offline maps require?

“Classic back-of-the-envelope type question.”

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Google·Software Engineer·Technical Phone Screen

Apr 2026
Google SWE interview, one question about how HTTP requests work. Pretty standard networking fundamentals stuff but still worth thinking through carefully.
  • Walk me through what happens when an HTTP request is made.

“I started with the DNS lookup and worked my way through TCP handshake, request headers, server processing, response codes.”

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Google·Machine Learning Engineer·Technical Phone Screen

Apr 2026
Google machine learning interview, one question about building a bot detection model. Not much context given so I'm going off what I remember.
  • How would you design and train a machine learning model to detect bots?

“This is the kind of question that sounds straightforward until you actually start talking through it.”

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Google·Software Engineer·Technical Phone Screen

Apr 2026
Google SWE coding round with a sequences problem that looks deceptively simple but has a neat theoretical underpinning. One question, pretty algorithmic, classic DP territory.
  • Given two finite sequences and an integer L, determine whether there exists a sequence of length at most L that contains both input sequences as subsequences. Return true or false.

“The key insight I kept circling around was connecting this to shortest common supersequence length.”

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Google·Data Scientist·Technical Phone Screen

Apr 2026
Google DS interview with a pretty gnarly statistics question about estimating rare query counts from sampled search logs. The whole thing was one multi-part problem that went deep fast.
  • You have a daily search query log and draw a 10% random sample. If you count queries that appear exactly once in the sample and multiply by 10 to estimate the number of singletons in the full log, is that estimate biased? Which direction, and why?
  • Using a frequency-of-frequencies framework, derive a better estimator for the true number of singleton queries. How do sampled frequency counts relate to population counts under binomial sampling, and what kind of estimator (Good-Turing style or similar) would you propose?
  • How would you compute standard errors for your singleton estimator, and how would you check whether your model assumptions are actually holding given that query frequencies are likely heavy-tailed?
  • Walk through a simulation plan to compare different singleton estimators under realistic search traffic distributions.

“This part felt approachable at first and then I second-guessed myself halfway through.”

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Google·Product Manager·Onsite - Product Sense / Strategy

Apr 2026
PM strategy question at Google, basically a CEO-in-the-room scenario about the Pixel hardware business. One question, no fluff.
  • You're the CEO of Google. Do you increase, decrease, maintain, or pause investment in the Pixel phone lineup, and why?

“This one took me a second to settle into because the CEO framing is a bit of a power move.”

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Google·Software Engineer·Onsite - Product Sense / Strategy

Apr 2026
Interviewed for a BA role at Google, got a product analytics question that sounds straightforward until you're actually sitting there trying to structure it on the fly.
  • If you noticed user engagement dropping, how would you go about figuring out why?

“I started with the obvious stuff, check if it's a data issue first, then segment by platform, user cohort, feature area.”

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Google·Software Engineer·Onsite - Coding / Algorithms

Apr 2026
Google SWE onsite coding round, one problem, classic Huffman tree construction. The interviewer wanted working code plus a discussion of the design, not just a solution dump.
  • Given a map of symbols to their frequencies, build a Huffman tree and produce a prefix-code encoding for each symbol.

“I knew this problem cold from prep so the heap-based build came out fine.”

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Google·Software Engineer·Onsite - Coding / Algorithms

Apr 2026
Google SWE coding round, PhD intern track. One meaty problem about log file intersection with a memory-constraint follow-up that turned into a whole conversation about external sort vs hashmap tradeoffs. Came out feeling okay but definitely underestimated how much the interviewer cared about defending the algorithmic choice.
  • You have two log files where each line contains a timestamp, an object ID, and a client ID. Find all object IDs that appear in both files and are associated with at least two distinct client IDs across both files combined.
  • What is the time and space complexity of your set-intersection approach on the two files?
  • Now assume neither file fits in memory. How do you redesign the solution?

“The base case felt manageable: scan each file into a hashmap keyed by object ID with a set of client IDs, then intersect the keys and filter by set size.”

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