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Google interview notes, page 190 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
Interviewed for a PM role at Google and got hit with a market sizing question about U.S. oil imports. Pretty standard estimation format but I underestimated how much structure they wanted.
  • Estimate how many barrels of oil the United States imports per year.

“I started by trying to break it down from daily consumption and work backward to imports, but I fumbled the math mid-answer and kind of lost the thread.”

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Google·Product Manager·Onsite - Product Sense / Strategy

Jun 2026
PM interview at Google, one question about launching a product. They wanted specifics, not hand-waving.
  • Walk me through a time you took a product from zero to launch.

“They pushed hard for specifics, which I wasn't fully ready for.”

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Google·Product Manager·Onsite - Product Sense / Strategy

Jun 2026
Google PM case question, pretty open-ended. A friend wants to start a cookie business and you have to build a go-to-market plan on the spot. Not a lot to go on, which is kind of the point.
  • Your friend wants to launch a cookie business. Build a plan to bring it to market.

“I spent probably too long trying to nail down the customer segment before moving to anything else.”

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Google·Data Scientist·Technical Phone Screen

Jun 2026
Pandas coding question for a Data Scientist role at Google. One problem, fairly involved, with a lot of edge cases baked in around NaN handling and operator precedence. Felt like a take-home but was probably a live technical screen.
  • Using pandas, add a new column 'risk_tier' to a DataFrame based on a strict hierarchy of conditions: 'high' if returns >= 2 OR last_review_rating <= 2.0, 'medium' if amount >= 200 AND country is US or CA, 'new' if signup_date falls within 30 days before 2025-09-01, and 'low' otherwise. Must be fully vectorized, handle NaNs in last_review_rating without incorrectly triggering 'high', and include a basic unit test.

“The NaN condition is the thing that gets you.”

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Google·Software Engineer·Technical Phone Screen

Jun 2026
Google SWE coding round, one problem the whole session. Subtraction on big number strings, no BigInteger allowed. Felt manageable at first but the edge cases piled up fast.
  • Given two non-negative integers as strings, compute their difference (num1 minus num2) and return the result as a string. You cannot convert the inputs to a native big integer type; you have to do the subtraction digit by digit. The result can be negative, and leading zeros should be stripped from the output.

“I started okay, figured out I needed to handle the sign first by comparing which string was larger, then do the actual subtraction on the bigger minus the smaller.”

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Google·Machine Learning Engineer·Onsite - Behavioral / Leadership

Jun 2026
Behavioral round at Google for an ML Engineer role, two questions, pretty standard stuff. They want real stories with specifics so vague answers will get you follow-up questions until you either produce the details or run out of road.
  • Tell me about a significant failure or mistake you made at work. Walk me through the situation, what you did, the outcome, and what you changed about how you work afterward.
  • Describe a time you had a serious conflict with a coworker, manager, or stakeholder. What was it about, how did you handle the conversation, and what came out of it?

“The follow-ups are where this gets uncomfortable.”

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Google·Software Engineer·Hiring Manager Screen

Jun 2026
Interviewed for a bizops role at Google, one question about stakeholder influence. Not much to report beyond that.
  • How would you go about convincing senior stakeholders to get behind a decision or initiative?

“This is basically the core bizops question and I still fumbled it a bit.”

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Google·Data Scientist·Technical Phone Screen

Jun 2026
Google data science coding round with two numerical/statistical problems. Both questions had a math-heavy flavor and required knowing your way around distributions and loss functions, not just writing clean code.
  • Write functions to sample from a truncated normal distribution under three different constraints: x greater than 1, x between 4 and 4.05, and x greater than 4.
  • Given an array X, find the value of theta that minimizes the sum of squared errors, then the sum of absolute errors, and finally derive what loss function would give you the 90th percentile as its solution.

“The first case was fine, rejection sampling works and you don't lose too many draws.”

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Google·Software Engineer·Technical Phone Screen

Jun 2026
Google SWE interview with a meaty design question about a feature activation engine. The problem had enough moving parts that I kept second-guessing myself on the deactivation cascade logic, but the graph fundamentals were solid ground to stand on.
  • Design a feature flag activation engine where each feature has prerequisite features that must be active before it can be enabled. Support activate, deactivate (with transitive cascade), and cycle detection operations.

“The activate part was fine, just check all prereqs in the active set.”

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Google·Software Engineer·Onsite - Coding / Algorithms

Jun 2026
Google SWE interview with a classic scheduling problem. Nothing too exotic but the heap approach tripped me up a bit under pressure.
  • Given an array of meeting time intervals, find the minimum number of conference rooms needed to schedule all meetings without conflicts.

“I knew there were two ways to go at this.”

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