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Amazon interview notes, page 127 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·Technical Phone Screen

Jun 2026
Amazon SWE coding round with a recursive schema validation problem. Pretty meaty for a single question, had to design the schema format myself and handle nested objects, arrays, and type checking all in one go.
  • Design and implement a schema validation system for JSON-like data. Given a data value and a schema definition you design yourself, return whether the data conforms to the schema. Must handle primitives, nested objects, and arrays recursively, with up to 100k total nodes.

“The part that tripped me up first was that they let me design the schema format, which felt like a gift but actually added pressure.”

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Amazon·Software Engineer·Onsite - System Design / Architecture

Jun 2026
Amazon SWE interview with a product design question that had nothing to do with code. Wasn't expecting that at all.
  • Design a chair for people with disabilities.

“Completely blindsided me.”

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Amazon·Software Engineer·Onsite - Coding / Algorithms

Jun 2026
Amazon SWE coding round, one question the whole time: shortest path in a grid using BFS. Pretty standard stuff but I still managed to second-guess myself halfway through.
  • Given a 2D grid where cells are either walkable or blocked, find the minimum number of steps to reach a target cell from a start cell using 4-directional movement. Return -1 if no path exists.

“I knew it was BFS immediately, which felt good, but then I wasted like two minutes debating whether to use a visited set or just mutate the grid in place.”

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Amazon·Software Engineer·Onsite - Behavioral / Leadership

Jun 2026
Behavioral loop at Amazon for a software engineer role, two questions that both had follow-ups depending on what projects you brought up. Pretty standard for Amazon but the GenAI question felt newer and caught me slightly off guard.
  • Tell me about a time you tackled a complex technical problem. What made it hard, how did you approach it, and what would you change looking back?
  • How do you use generative AI tools in your day-to-day engineering work, and how do you make sure the output is actually correct and safe to use?

“I had a story ready but fumbled explaining the actual complexity.”

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Amazon·Software Engineer·Onsite - Behavioral / Leadership

Jun 2026
Amazon final-round behavioral for a Data Engineer internship. Three questions, all pretty standard leadership-principle territory, but the 'team was blocked' one tripped me up more than I expected.
  • Describe a significant challenge or difficult problem you faced on a project, internship, or research assignment. What did you do and what came of it?
  • Tell me about a time your team was stuck or struggling to execute. How did you get everyone aligned and push things forward?
  • Why Amazon, and why does this role fit where you are in your career right now?

“I went with a research project where the data pipeline kept breaking mid-run.”

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Amazon·Software Engineer·Onsite - Behavioral / Leadership

Jun 2026
Amazon behavioral prep session focused on ownership and decision-making under pressure. Two classic leadership principle questions, the kind that sound simple until you actually have to tell a real story with real stakes.
  • Tell me about a time you went beyond your formal responsibilities to get something important done.
  • Tell me about a time you had a tight deadline and made a risky decision. How did you think through the risk, communicate it, and what was the result?

“The tricky part isn't finding a story, it's making sure you don't accidentally make yourself sound like you steamrolled whoever actually owned the thing.”

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Amazon·Machine Learning Engineer·Technical Phone Screen

Jun 2026
Amazon ML Engineer technical screen, one coding problem the whole time. The prompt was about building a schema validator for nested JSON structures and I spent most of the session just trying not to miss edge cases.
  • Given a JSON-like data structure and a schema definition, implement a validator that checks whether the data conforms to the schema. The schema supports nested objects, lists, and primitive types, and you need to handle missing fields, extra fields, type mismatches, and nullable fields.

“Recursion itself wasn't the hard part.”

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Amazon·Software Engineer·Hiring Manager Screen

Jun 2026
Amazon SDE hiring manager screen that went sideways fast. HR said to expect two standard coding rounds with behavioral questions, but the actual interview was nothing like that. The HM was friendly enough, cracked a joke at the start, then got pretty serious. It ended abruptly when someone pulled him away for a meeting mid-Q&A, so the whole thing felt unresolved.
  • How do you use generative AI tools in your day-to-day engineering work, and how do you make sure the code they produce is actually correct?

“This came near the end and I wasn't really ready for it.”

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Amazon·Software Engineer·Online Assessment (OA)

Jun 2026
Amazon SWE coding round, one algorithmic problem the whole time. Not a bad experience but the problem had some edge cases I didn't fully think through until after I submitted.
  • Given an integer array, you can repeatedly select any contiguous subarray and increment all its elements by 1. What is the minimum number of such operations needed to make the array non-decreasing?

“Spent probably too long trying to brute force a greedy approach before realizing you can think about it in terms of the 'deficit' at each position relative to the previous element.”

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Amazon·Software Engineer·Onsite - Behavioral / Leadership

Jun 2026
Interviewed for a PMM role at Amazon, just the one behavioral question from what I can tell. Pretty standard retrospective-style prompt but it has more depth to it than you'd expect.
  • Tell me about a project you completed successfully, and what you would do differently if you ran it again.

“I picked a product launch I was proud of and spent too long on the win part.”

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