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Google interview notes, page 136 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·Product Manager·Onsite - Product Sense / Strategy

Jun 2026
Google PM interview with a classic 'critique an app you hate' question that spirals into strategy and metrics. Pretty standard format but the follow-up about measurement tripped me up more than I expected.
  • Name an app that's widely popular but that you personally dislike. As the PM of that app, what would you change, and how would you measure whether those changes worked?

“The first part is fun until you realize you're about to trash a product in front of someone who might love it.”

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Google·Data Scientist·Technical Phone Screen

Jun 2026
Google Data Scientist interview with a meaty ML modeling question centered on customer retention for a B2B product. One question but it had a lot of layers, probably 30+ minutes of back and forth on it.
  • Walk through how you'd build a model to predict whether an enterprise customer will renew their contract. What features would you engineer, when would logistic regression be enough versus something like gradient boosting or a neural net, and how would you measure whether the model is actually working?

“I started with feature engineering which felt like the right move, talked about usage signals, contract age, support ticket volume, things like that.”

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Google·Machine Learning Engineer·Technical Phone Screen

Jun 2026
Google ML Engineer interview with a conveyor belt / streaming data problem. The core challenge was extending a sorted-window approach to handle items arriving in arbitrary order, which made it more interesting than the warmup version.
  • Items arrive one at a time on a conveyor belt, each with a numeric size. Given a threshold T, detect and return any triple of items whose max minus min is less than T, as soon as such a triple exists in the arrival history. Items arrive in arbitrary order. How do you solve this incrementally?

“The part 1 warmup (sorted arrivals) felt manageable since you just check the last three items each time.”

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Google·Product Manager·Onsite - Product Sense / Strategy

Jun 2026
PM interview at Google with a metrics-heavy product sense question. Just the one question from what I remember, but it took up the whole session.
  • You're the GM of Google Photos putting together a one-page document for leadership. What key metrics do you include?

“I went straight for engagement metrics and blanked on the business side for a good 30 seconds.”

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Google·Data Scientist·Onsite - Behavioral / Leadership

Jun 2026
Interviewed for a Data Scientist role at Google and got hit with a deep behavioral question about research. Not the vibe I expected but it made sense in retrospect.
  • Tell me about the most challenging research project you've worked on. What made it hard, how did you structure your goals and milestones, what obstacles came up, and how did you get it across the finish line?

“This question is deceptively large.”

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Google·Software Engineer·Technical Phone Screen

Jun 2026
Google SWE coding round, two parts to the same problem. The array version was fine, the stream follow-up is where things got interesting.
  • Given an array and an integer offset, return the first N elements if offset is positive, or the last N elements if offset is negative. For example, offset 2 on a five-element array gives you the first two; offset -3 gives you the last three.
  • Now implement the same offset behavior but the input is a stream with hasNext() and getNext() methods. You don't know the size in advance. How do you handle both the positive and negative offset cases?

“Pretty clean to start.”

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Google·Software Engineer·Onsite - Behavioral / Leadership

Jun 2026
Interviewed for a bizops role at Google, one behavioral round with a focus on how you've handled data-heavy work. Pretty standard stuff but the question had more depth to it than I expected.
  • Can you walk me through a time you worked on a project that involved a large amount of data?

“I had an answer ready but I kind of rambled into the weeds on the technical setup instead of keeping it focused on what I actually did and why it mattered.”

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Google·Software Engineer·Onsite - System Design / Architecture

Jun 2026
Google SWE system design round, one question, no fluff. They just dropped a distributed file system prompt and let you run with it.
  • Design a distributed file system.

“I started with the usual stuff, namespace, metadata server, chunked storage, replication.”

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Google·Data Scientist·Technical Phone Screen

Jun 2026
Statistics-heavy technical phone screen for a Data Scientist role at Google. Four questions back to back, all focused on probability and estimation fundamentals. Not a coding round at all, which I wasn't fully expecting.
  • Given an i.i.d. sample from a Normal distribution, derive the maximum-likelihood estimators for the mean and variance.
  • For two random variables X and Y with a known joint pdf, derive the conditional distribution of X given Y.
  • Write out the pdf and cdf of the standard Normal distribution.
  • Starting from a concrete estimation problem, what assumptions are needed for an estimator to be unbiased, and how do you justify each one?

“This is the kind of question that looks easy on paper and then you second-guess yourself mid-derivation.”

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Google·Software Engineer·Technical Phone Screen

Jun 2026
Google SWE coding round with a graph/scheduling problem that looks deceptively simple until you realize the time-feasibility constraint changes everything. No behavioral stuff, just the one problem with a few test cases to work through.
  • Given a list of flights each with an origin, destination, departure time, and arrival time, determine whether a time-feasible itinerary exists from a start airport to an end airport. A connection is only valid if the next flight departs no earlier than the previous flight's arrival time.

“My first instinct was BFS and I went with it, but the tricky part is that the same airport can be visited at different times and you can't just track visited nodes like a normal graph problem.”

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