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Google interview notes, page 124 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·Machine Learning Engineer·Technical Phone Screen

Jun 2026
Google MLE interview that centered on a weighted random sampling problem. The base version was straightforward but the follow-up is what actually tested whether you understood the tradeoffs, not just the happy path.
  • Given an array of positive integer weights, implement a function that randomly picks an index where the probability of picking index i equals w[i] divided by the total sum of weights.
  • How would you redesign your solution if the weights can change frequently, making the prefix-sum and binary-search approach impractical? Walk through the tradeoffs between approaches.

“Prefix sums plus binary search, pretty standard if you've seen it before.”

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Google·Software Engineer·Technical Phone Screen

Jun 2026
Google technical phone screen, graph problem that started straightforward and then got progressively more interesting once the follow-up kicked in. Felt okay about it but the union-find discussion at the end was where I was least confident.
  • You're given a list of nodes, each with a primary ID and a secondary attribute like a color or label. Two nodes are connected if they share that attribute. Build an adjacency list and run BFS from a source node to answer reachability or shortest-path queries.
  • Follow-up: when would you use union-find instead of the attribute-keyed map approach, and what are the tradeoffs?

“Started with the naive O(n^2) pairwise comparison and they let me run with it for a bit before asking if I could do better.”

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Google·Product Manager·Onsite - Product Sense / Strategy

Jun 2026
PM strategy round at Google, one question about whether and how Google should move into the metaverse space. Pretty open-ended, which sounds fun until you're actually sitting there trying to figure out where to start.
  • Should Google enter the metaverse space? If so, how would you approach it?

“I spent the first minute just trying to define what 'metaverse' even means in this context because it's such a loaded word.”

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Google·Data Scientist·Onsite - Behavioral / Leadership

Jun 2026
Behavioral round for a Data Scientist role at Google. Four questions, all pretty classic but a couple of them had enough nuance to trip you up if you weren't ready.
  • Tell me about yourself and why you think you're a strong fit for this role at Google.
  • Walk me through a problem your team ran into. How did you find it, figure out what was causing it, and help fix it?
  • If a project you were working on missed its deadline, how would you communicate that to stakeholders and what would you do differently next time?
  • Your team is planning a group outdoor activity to build cohesion, but a few teammates don't want to join. How do you handle it?

“Went fine.”

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Google·Product Manager·Onsite - Product Sense / Strategy

Jun 2026
Google product design interview, one question about building an app recommendation system for mobile. Pretty open-ended, which I wasn't fully prepared for.
  • How would you design an app suggestion system for smartphones?

“I started with user context, like time of day and usage patterns, which felt right, but I rambled for a while before getting to any concrete product structure.”

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Google·Data Scientist·Technical Phone Screen

Jun 2026
Got a technical screen for a Data Scientist role at Google and the SQL question they threw at me was genuinely one of the harder ones I've seen. Single query, no temp tables, identity merges, gap logic, churn cutoffs. I survived but I'm not sure I'd call it clean.
  • Given three PostgreSQL tables for user profiles, events, and identity merges, write a single SQL query using only CTEs and window functions to compute monthly cohort retention for cohorts from January through March 2025, covering months k=0 through k=3. The query must consolidate merged user identities (treating all child events as belonging to the parent), filter to eligible US non-employee non-test users, define activity as having at least one app_open or purchase event in a calendar month, and apply a retention rule where a user is retained in month k only if the gap between their first qualifying event that month and their previous non-refund event is 35 days or fewer. Additionally, once a user exceeds a 60-day gap, they should not be counted as retained in any subsequent month.

“I stared at this for a solid minute before writing anything.”

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Google·Data Scientist·Technical Phone Screen

Jun 2026
Had a Data Scientist interview at Google where the focus was on explaining ML concepts to a non-technical audience and handling messy real-world data problems. Pretty conceptual for a tech company but it made sense given the scenario they set up.
  • How would you explain linear regression to someone who has no background in data or statistics?
  • Your dataset is heavily imbalanced. Walk through the techniques you'd consider to still build a model that actually works, and explain why you'd choose each.

“I went with the 'best fit line through scattered points' analogy and tried to connect it to something intuitive like predicting rent based on apartment size.”

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Google·Product Manager·Onsite - Product Sense / Strategy

Jun 2026
Google PM interview with a single product design question focused on accessibility. Pretty open-ended, which sounds nice until you're sitting there trying to figure out where to start.
  • How would you design a bookshelf specifically for elderly users?

“I went straight to physical ergonomics, adjustable heights, easy-grip handles, that kind of thing.”

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Google·Software Engineer·Onsite - Multi Round

Jun 2026
Three rounds of technical interviews at Google for a new grad software engineering role, covering graphs, trees, and matrix problems. The coding went fine but the behavioral portions tripped me up more than expected, and the recruiter feedback confirmed what I already suspected.
  • Solve a hard graph problem, starting with a brute-force approach and then optimizing it.
  • Work through a challenging tree or graph problem, again presenting a brute-force solution before arriving at an optimized one.
  • Solve a matrix operation problem with multiple follow-up questions, aiming for an optimal solution.
  • Describe how you collaborate with others and demonstrate the mindset and values you'd bring to a team environment.

“Managed to get through both the naive and optimized versions, and the follow-ups weren't too bad.”

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Google·Software Engineer·Technical Phone Screen

Jun 2026
Google SWE interview with a matching problem that had a few layers to it. Not the hardest question I've seen but the follow-ups caught me a bit flat-footed.
  • You have a group of people and a group of questions, each with a set of tags. Match people to the questions they are qualified to answer. Start with a brute-force approach, then optimize.
  • Now assume questions have priorities. How do you make sure each person picks the highest-priority question they qualify for?

“The brute-force part was fine, just iterate over every person-question pair and check tag overlap.”

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