← Back to Directory

Google interview notes, page 128 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

Jun 2026
Google frontend engineer interview, got asked a pretty classic JS array manipulation problem. Nothing too wild but it still made me think more than I expected.
  • Given an array of objects, how would you remove duplicate entries?

“Seemed simple at first and I jumped straight to using a Map keyed by some unique property.”

View Post

Google·Software Engineer·Technical Phone Screen

Jun 2026
Google SWE interview with an OOD coding problem that had a spicy follow-up. The core design wasn't too bad but the weighted random sampling piece is where things got interesting.
  • Design a data collection backed by a hash map that supports inserting items and querying them sorted by frequency of insertion.
  • Follow-up: add a method that returns a random item where the probability of selecting each item is proportional to how frequently it was inserted. How would you implement this?

“Felt pretty comfortable here.”

View Post

Google·Data Scientist·Technical Phone Screen

Jun 2026
Google DS interview that went deep into mixed-effects modeling for a multi-level user outcome problem. Four connected questions, all building on the same scenario. Solid technical round, the kind where you really feel whether your stats foundations are there or not.
  • What is the difference between fixed effects and random effects in a statistical model?
  • Write out a mixed-effects model that estimates the impact of a product change while accounting for variability at both the user level and the country level.
  • When would you use a mixed-effects model instead of a plain regression with country and user dummy variables?
  • What are the main assumptions and potential pitfalls of mixed-effects models, and how would you validate that your model is reasonable?

“Knew this conceptually but stumbled trying to articulate it cleanly under pressure.”

View Post

Google·Software Engineer·Technical Phone Screen

Jun 2026
Google Data Engineer interview with a coding problem that looked straightforward until I actually tried to explain my reasoning out loud. The question was about counting index pairs with specific parity constraints, and the whole thing hinged on whether you could see past the brute force.
  • Given an integer array, count pairs (i, j) where i < j, the product of the two elements is even, and the difference j minus i is odd. Can you derive a counting formula that avoids checking every pair?
  • What variables would you track to implement this in a single pass, and what does your final formula look like?
  • What is the time and space complexity, and what edge cases would you test?
  • How would your approach change if the product had to be odd instead of even, or if j minus i had to be even instead of odd?
  • Could you solve this if the input arrived as a stream with indices assigned in order, rather than a fixed array?

“I started with brute force and the interviewer let me finish before asking if I could do better.”

View Post

Google·Software Engineer·Onsite - System Design / Architecture

Jun 2026
Google SWE ML system design round, roughly 60 minutes, covering recommender systems, vision classifiers, chatbots, and video deduplication at scale. The bar was higher than I expected, especially around cold start and post-launch monitoring.
  • Design a real-time recommendation system for an e-commerce or social platform.
  • Design a mobile app that lets users photograph a leaf and identify its species from a large set of possibilities.
  • Design a chatbot that can answer questions grounded in both structured and unstructured data sources.
  • Design a system to detect duplicate or near-duplicate short videos at billions-of-videos scale.
  • How would you handle cold start for new users or new items in a recommendation system?
  • How would you monitor a deployed ML system and handle issues like prediction drift, training-serving skew, or fairness regressions?
  • Walk through how you would set up an A/B test and holdback experiment to evaluate a new ranking model.
  • How would you trade off latency versus accuracy in an online serving system, and what techniques would you use to stay within a latency SLO?

“I went straight into two-tower retrieval and the interviewer seemed fine with it, but I fumbled when they pushed on freshness requirements.”

View Post

Google·Software Engineer·Technical Phone Screen

Jun 2026
Google SWE interview with a graph/BFS problem that had two parts and a follow-up. The problem was dressed up as delivery route planning but it's basically shortest path with constraints. Felt manageable until part two where the optimization criteria got layered.
  • Given a set of delivery stops and routes (where adjacent stops in a route are connected), find the minimum number of steps from a source stop to a target stop without passing through any dangerous stops. Return -1 if source or target is dangerous, or if no safe path exists.
  • Extend the previous problem: now you can pass through dangerous stops. Among all paths from source to target, first minimize the number of dangerous stops visited, then minimize the number of steps. How do you find the optimal path under this two-level priority?
  • How would you change your visited structure from part one to correctly handle the two-criteria optimization in part two, specifically accounting for cases where the same stop can be reached with fewer dangerous stops or fewer total steps?

“Pretty standard BFS once you see it.”

View Post

Google·Product Manager·Onsite - Cross-functional / Panel

Jun 2026
Interviewed at Google, got asked about navigating the tension between what engineers say is possible and what customers actually want. Not a lot of context to go on, but it's a question that sounds easy until you're actually in it.
  • How do you balance engineering limitations with customer requirements?

“I went straight to a past project where we had to cut a feature mid-sprint because the infra couldn't support it at scale.”

View Post

Google·Software Engineer·Onsite - Multi Round

Jun 2026
Went through the first part of a Google SWE loop, covering a behavioral round and one coding round. The behavioral went well, but the DSA round was a mess of half-finished code and a ticking clock. Two more rounds still to go, so the verdict is very much up in the air.
  • Tell me about a time you had to build something quickly with limited resources or under pressure.
  • Given a scheduling or ordering problem, find an optimal solution using an appropriate data structure.

“Coming from a startup, I had plenty of real material here.”

View Post

Google·Product Manager·Onsite - Product Sense / Strategy

Jun 2026
Got a product sense question at Google for a PM role that felt more like a economics/policy brain teaser than anything I'd prepped for. Short interview, one question, left me second-guessing my answer the whole ride home.
  • If you could remove one U.S. dollar bill denomination from circulation, which would you eliminate and why?

“I went with the $1 bill, leaned into the cost-to-produce argument and digital payments trends.”

View Post

Google·Product Manager·Onsite - Product Sense / Strategy

Jun 2026
Google PM interview with a product design question about building an app for a theme park. Pretty open-ended, which sounds fun until you're actually sitting there trying to figure out where to start.
  • Design an app for a theme park. Walk through your product thinking.

“I spent probably too long on user segmentation before even touching the problem.”

View Post