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Google interview notes, page 106 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 SWE coding round, one question the whole time and it was a graph problem dressed up in grid clothing. Felt pretty solid on the approach but took me a minute to see the connection to a classic problem.
  • Given a 2D grid of integers, find a path from the top-left cell to the bottom-right cell (moving in four directions) that minimizes the maximum cell value encountered along the way. Return that minimum possible maximum value.

“I knew BFS was involved but my first instinct was to binary search on the answer and check reachability at each candidate value.”

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Google·Data Scientist·Technical Phone Screen

May 2026
Interviewed for a data scientist role at Google and got hit with a pretty deep causal inference question about weather and mental health. Not a typical metrics or A/B testing setup, which threw me a bit.
  • How would you design a study to estimate the causal effect of weather conditions on mental health outcomes? Walk through your study design choices, the confounders you'd worry about (like seasonality, geography, and socioeconomic status), how you'd handle them, and how you'd validate and interpret your findings.

“I started with observational study design and immediately flagged seasonality as the big confounder, which felt right, but then I kind of stumbled when they pushed on geography and SES together.”

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Google·Machine Learning Engineer·Technical Phone Screen

May 2026
Screened for an ML engineer role at Google, just one question about ML types. Pretty surface level, not much to say.
  • Can you walk through the different types of machine learning?

“Covered supervised, unsupervised, and reinforcement learning.”

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Google·Technical Program Manager·Onsite - Behavioral / Leadership

May 2026
Interviewed for a TPM role at Google, came across a risk management question that felt pretty standard but still tripped me up a bit in the moment.
  • Walk me through how you've managed risk on a project.

“I had an answer ready but it came out kind of scattered.”

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Google·Product Manager·Onsite - Product Sense / Strategy

May 2026
Google product sense interview, one question about improving an existing feature. Pretty open-ended and I spent way too long setting up context before actually saying anything useful.
  • How would you improve Google Docs's real-time collaboration feature?

“I went straight to listing features and the interviewer just kind of waited.”

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Google·Product Manager·Onsite - Product Sense / Strategy

May 2026
Google PM interview with a visual prompt exercise. They showed a picture and asked you to reason through a design decision, which sounds simple but puts you on the spot fast.
  • You're shown a photo of a car with a mirror mounted on the bonnet. Why do you think it was designed that way?

“Blanked for a second because I kept trying to think of it as a safety feature and couldn't make that work.”

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Google·Product Manager·Onsite - Product Sense / Strategy

May 2026
Google interview with a market sizing question about the bike-based food delivery industry. Pretty short on details from whoever posted this, but the question itself is a classic estimation case.
  • Estimate the total size of the food delivery market that operates via bicycle couriers.

“These estimation questions always feel deceptively simple and then you're standing there trying to remember if you should start from supply or demand.”

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Google·Software Engineer·Technical Phone Screen

May 2026
Google SWE coding round with a streaming subarray problem. Not much context shared beyond the problem itself, but it's the kind of question that looks approachable until you actually have to output the answer after every single input.
  • Given a stream of integers arriving one at a time, after each new integer is received, find the length of the longest contiguous subarray whose average equals a target value S.

“The online part is what makes this tricky.”

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Google·Data Scientist·Technical Phone Screen

May 2026
Google DS interview with a product metrics and experimentation question centered on a chat auto-reply feature. Single question but it had a lot of moving parts, felt like they wanted to see how you'd structure an ambiguous product problem end to end.
  • For a chat app's auto-reply suggestion feature, define the primary success and guardrail metrics, design an experiment to measure its impact, and describe what diagnostics you'd use if the results were inconclusive.

“This one sprawled in ways I didn't expect.”

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Google·Software Engineer·Technical Phone Screen

May 2026
Google coding interview, one tree problem, not much else to go on but it was enough to make me second-guess my fundamentals for a week.
  • Given a list of binary tree nodes in shuffled order, find and return the root node.

“My first instinct was to just look for the node that nobody points to as a child, which is the right idea, but I fumbled explaining it cleanly.”

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