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Amazon interview notes, page 5 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 - Coding / Algorithms

Apr 2026
Amazon SWE coding round focused entirely on object-oriented design, specifically a multi-level parking lot system. The scope was broader than a typical LeetCode session, covering class design, implementation, and a design discussion all in one go.
  • Design a multi-level parking lot system that supports different vehicle types and spot sizes. Identify the main entities, sketch a class diagram with clear responsibilities, fields, and methods, and implement the core classes with attention to encapsulation and extensibility.
  • Implement park(vehicle), leave(vehicle), and availability_summary() methods. How do you handle spot assignment and freeing spots for vehicles that occupy multiple spots?
  • Walk through your design and discuss how it would accommodate future requirements like hourly billing, reservations, EV-charging spots, and multi-tenant lots.

“I started with the entities and drew out ParkingLot, Level, ParkingSpot, and the Vehicle hierarchy pretty quickly.”

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Amazon·Software Engineer·Onsite - System Design / Architecture

Apr 2026
Amazon solutions architect interview with a classic distributed systems troubleshooting question. Short session, one scenario-based problem, felt more like a screen than a full design round.
  • You have a web app sitting behind a load balancer with two backend servers. Users are occasionally getting logged out mid-session. What's going on?

“Jumped straight to session stickiness and they seemed pleased, but I fumbled when they asked me to go further.”

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Amazon·Software Engineer·Onsite - Coding / Algorithms

Apr 2026
Amazon SWE coding round focused on sorting algorithms from scratch, no built-ins allowed. They pushed pretty hard on the tradeoffs between a naive approach and something more production-ready, which I wasn't totally expecting for what sounded like a simple warmup.
  • Sort an array of numbers in place without using any built-in sort functions. Walk through at least two different algorithms, compare their tradeoffs, and explain which you'd actually use in production.
  • How would you modify your sorting approach to sort in descending order, or sort by a custom comparator?
  • How does your algorithm handle duplicates or nearly-sorted input? Does your choice of algorithm change?

“I started with insertion sort because it's the one I can actually explain without blanking, then moved to quicksort for the in-place O(n log n) angle.”

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Amazon·Software Engineer·Technical Phone Screen

Apr 2026
Amazon SWE interview that was basically a log parsing system design disguised as a coding question. More depth than I expected for what looked like a straightforward parsing task.
  • Given a stream of application log lines in a structured format, parse each line into a record with fields like timestamp, level, source, message, and key-value attributes. How would you implement this parser?
  • How would you handle malformed log lines and multi-line stack traces that continue from a previous log entry?
  • Implement a filter function that queries parsed log records by time range, log level, source, and attribute key-value matches.
  • Implement count_by and top_k functions that aggregate log records by any field or attribute key, returning frequency counts or the k most common values.
  • What data structures would you choose to support efficient querying across a large volume of log records, and what are the complexity tradeoffs?

“The format looked clean on paper but the edge cases piled up fast.”

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Amazon·Machine Learning Engineer·Technical Phone Screen

Apr 2026
Amazon ML engineer interview, got a classic imbalance question that sounds straightforward but has a lot of directions it can go.
  • Your training data is heavily imbalanced. What techniques would you use to prevent the model from overfitting to the majority class?

“I started with the obvious stuff, resampling and class weights, but then they pushed on whether those were actually solving overfitting or just shifting the decision boundary.”

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Amazon·Product Manager·Onsite - Behavioral / Leadership

Apr 2026
Amazon PM interview with a budget management question that's pretty standard for the role but still tripped me up a bit in the moment.
  • When you're managing a budget with limited resources, how do you maximize what you can get out of it?

“I went straight to prioritization frameworks and talked about cutting low-ROI work first.”

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Amazon·Product Manager·Onsite - Product Sense / Strategy

Apr 2026
PM interview at Amazon, just the one question about COVID's impact on the business. Pretty thin as far as interviews go.
  • How did COVID-19 affect Amazon as a business?

“This is one of those questions that sounds easy but you can go in a lot of directions.”

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Amazon·Software Engineer·Onsite - Behavioral / Leadership

Apr 2026
Interviewed for a solutions architect role at Amazon and got hit with a behavioral question about personal relationships, which felt a bit unexpected for a technical role.
  • Who is the most important person in your life and why?

“Wasn't expecting this one in a solutions architect loop.”

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Amazon·Product Manager·Onsite - Product Sense / Strategy

Apr 2026
Amazon PM interview with a metrics question focused on an enterprise messaging product for admin users. Pretty niche framing that tripped me up a bit.
  • How would you define and measure success for the insights feature of an enterprise messaging app, specifically from the perspective of admin users?

“The admin angle is what made this harder than it looks.”

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Amazon·Software Engineer·Onsite - Behavioral / Leadership

Apr 2026
Amazon SWE interview with a scenario-based section where you rate different response options to a teammate situation. Pretty unusual format, felt more like a workplace judgment test than a technical screen.
  • A teammate is still running into errors with an API you built after you've already given her guidance. You're shown several possible responses ranked from most to least effective. How do you evaluate which approach is right?

“The setup is you pick between options like sitting with her for an hour and looping in a senior engineer if it's still broken, versus just sending docs and hoping for the best, versus pointing her at the documentation and walking away.”

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