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Amazon interview notes, page 58 of 159

FAANG / Big Tech

Most of these Amazon write-ups come from Software Engineers (1,092), then Data Scientists (182) and Product Managers (165). The most reported rounds are the technical phone screen (618), the behavioral / leadership round (309), the online assessment (228) and the system design round (159).

1581 interview notes · updated Oct 2026

Amazon·Software Engineer·Onsite - System Design / Architecture

May 2026
Amazon system design round for a software engineer role. The whole session was basically one deep question about rate limiting, and they really wanted you to get into the weeds on trade-offs, not just sketch out a solution.
  • Design a rate limiter that accepts or rejects incoming requests per user, given a configurable time window and a max request count. Implement the core data structure and an allow_request(user_id, timestamp) API, and walk through the memory implications and the trade-offs between sliding-window and fixed-window approaches.

“I started with a fixed-window counter because it's simple and I could code it fast, but they pushed back almost immediately asking about edge cases at window boundaries.”

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Amazon·Software Engineer·Onsite - Coding / Algorithms

May 2026
Amazon SWE interview that leaned heavily on algorithms and system design. Two meaty problems back to back, both with follow-ups on concurrency and trade-offs that I wasn't fully ready for.
  • Using a sliding window approach, design an algorithm to find the maximum sum of any contiguous subarray of length k. Walk through the time and space complexity, and explain how you'd adapt it for concurrent execution.
  • Design a system to maintain the running median of the last k integers in a real-time data stream. Which data structures would you use, and what are the trade-offs between them?

“The sliding window part itself was fine, add the new element drop the old one, O(n) time O(1) space, standard stuff.”

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Amazon·Software Engineer·Onsite - System Design / Architecture

May 2026
Amazon system design screen focused on building a home recommendation feature similar to what you'd see on Airbnb. Pretty classic ML-adjacent product design question but it has more depth than it first appears.
  • Design a system that recommends similar home listings to users on a platform like Airbnb.

“I went straight to collaborative filtering and the interviewer kind of just waited.”

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Amazon·Software Engineer·Technical Phone Screen

May 2026
Amazon coding screen, pretty standard stuff. One question, sliding window, done.
  • Given a string, find the length of the longest substring with no repeating characters.

“Classic sliding window.”

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Amazon·Data Scientist·Technical Phone Screen

May 2026
Amazon DS interview for Alexa Shopping, essentially one long case study about diagnosing a KPI drop and then convincing people to act on it. Dense question, lots of moving parts, felt like they were testing whether you'd panic or actually structure your thinking under pressure.
  • A key voice checkout conversion metric for Alexa Shopping suddenly drops. How do you diagnose the root cause, from scoping the problem all the way through to building a causal analysis?
  • Once you've identified the likely cause, how do you design the actual analyses to confirm it? Walk through funnel breakpoints, feature flag comparisons, deploy diffs, and rollback tests.
  • What short-term mitigations would you propose while the investigation is still ongoing, and what longer-term fixes would you plan for after the root cause is confirmed?
  • How would you persuade skeptical stakeholders to act on your findings? Specifically: how do you structure the decision document, quantify impact, handle pushback on risk, and get clear ownership assigned?
  • After the fix is deployed, how do you measure whether it actually worked, and what do you put in place to prevent the same thing from happening again?

“This one sprawls fast if you're not careful.”

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Amazon·Software Engineer·Onsite - Behavioral / Leadership

May 2026
Behavioral round at Amazon for a software engineer role. One question but they really dug into it, way more follow-ups than I expected.
  • Tell me about a time you had to deliver a project under a tight deadline. What tradeoffs did you make, and how did you keep quality from slipping?

“I had a decent story ready but the follow-ups are where it got uncomfortable.”

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Amazon·Software Engineer·Onsite - Behavioral / Leadership

May 2026
Two behavioral questions at Amazon for a software engineer role, both AI-focused. Pretty niche compared to the usual leadership principle stuff I was expecting.
  • Tell me about a time you used AI to simplify or reduce the complexity of a solution you were working on.
  • Can you give an example of how you regularly incorporate AI tools into your day-to-day work?

“I had a decent example ready but the word 'simplify' tripped me up a bit.”

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Amazon·Product Manager·Onsite - Product Sense / Strategy

May 2026
Amazon PM interview, one question about cross-platform growth strategy between Prime and Twitch. Not much context to go on but it's a solid product strategy prompt.
  • What cross-promotion opportunities exist between Amazon Prime and Twitch to grow both platforms?

“This is the kind of question where you can go a dozen directions and that's kind of the problem.”

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Amazon·Software Engineer·Onsite - System Design / Architecture

May 2026
Amazon system design round focused entirely on building an autocomplete system from scratch. Pretty deep technically, they wanted actual implementation details not just hand-waving at tries.
  • Design and implement an autocomplete component that takes a history of query strings with associated frequencies and returns the top K suggestions for a given prefix in real time. Your solution should support frequency updates for repeated queries and insertion of new queries.
  • How would you handle deletions, streaming updates at scale, and memory pressure in your autocomplete system?

“This was the whole interview, basically.”

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Amazon·Software Engineer·Online Assessment (OA)

May 2026
Took an Amazon OA that had a DSA problem and an AI-assisted debugging section. Finished the DSA fully but only got 3 out of 6 on the debugging part, now just waiting to hear back.
  • Solve a DSA problem as part of the online assessment.
  • Complete an AI-assisted debugging section where you identify and fix issues in given code.

“Got through this one completely, no issues.”

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