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Amazon interview notes, page 96 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·Online Assessment (OA)

Jun 2026
Amazon SWE online assessment with a simulation-style coding problem involving priority queues and some arithmetic logic. Nothing behavioral, just straight into the problem.
  • You have n VM types each with a limited stock count. Customers arrive one by one and always rent from the VM type with the highest current availability. Each rental costs the sum of the current lowest non-zero availability and the highest availability across all VM types. After each rental, that VM type's count drops by 1. Find the total revenue after serving all m customers.

“The revenue formula tripped me up at first.”

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Amazon·Software Engineer·Technical Phone Screen

Jun 2026
Amazon coding interview with a dynamic programming / backtracking problem that looks deceptively like a simple greedy but really isn't. One question, fairly meaty, and the memoization angle is where things get interesting.
  • Given a list of item prices, a set of bundle deals (each specifying quantities of items and a bundle price), and a target quantity for each item, find the minimum total cost to exactly fulfill the shopping list. Bundles can be reused but you can't buy more of any item than needed. Items not in any bundle are purchased at unit price.

“The greedy instinct here is to just grab the best bundle repeatedly, and that will absolutely fail on certain inputs.”

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Amazon·Software Engineer·Technical Phone Screen

Jun 2026
Amazon Applied Scientist interview with a greedy algorithm problem around task scheduling under a time budget. Pretty standard stuff if you've seen this pattern before, but the setup takes a minute to parse.
  • Given two arrays of equal length where one represents total time required for each task and the other represents time already spent, and given a total additional time budget, how do you allocate the budget to maximize the number of tasks you can complete?

“The key move is computing the remaining time per task and sorting ascending, then greedily finishing the cheapest tasks first until the budget runs out.”

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Amazon·Software Engineer·Onsite - Multi Round

Jun 2026
Amazon software engineer interview where I got hit with two very different formats back to back. One interviewer wanted a classic algorithm problem, the other wanted hand-written front-end code on a whiteboard. Switching gears between those two mindsets in the same loop was harder than I expected.
  • When an interviewer asks for an algorithmic solution, how do you clarify the problem requirements, pick the right data structures, and handle edge cases before writing code?
  • When asked to write front-end code by hand during an interview, how do you approach clarifying browser API constraints, structuring your solution, and validating edge cases without running the code?

“I always try to restate the problem back before touching any code.”

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Amazon·Software Engineer·Onsite - System Design / Architecture

Jun 2026
Amazon system design round for a software engineer role. One question, pretty involved, covering class design all the way through thread safety and extensibility. Not a bad experience but I definitely underestimated how far they'd want to push the design.
  • Design an in-memory publish-subscribe system using object-oriented principles. Cover the main classes and public APIs, how message delivery works, thread safety, and how you'd extend it for retries, persistence, or async delivery.

“I started with the obvious stuff: a Topic class, a Subscriber interface, a Publisher.”

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Amazon·Software Engineer·Onsite - Coding / Algorithms

Jun 2026
Amazon SWE coding round, one problem the whole session. The question was a warehouse simulation thing with some tricky edge cases around when to skip turns. Felt like it was testing greedy thinking more than anything else.
  • You have n warehouses with given inventory levels. Each turn, you dispatch a fixed amount from a warehouse, then your co-worker dispatches their fixed amount. The co-worker can skip their turn up to a limited number of times total, and when they skip, you immediately take another dispatch. You earn a credit for a warehouse only if it hits zero or below right after your dispatch. Design and implement an algorithm that maximizes total credits by choosing optimally when the co-worker skips.

“Took me a while to even understand what 'credit' meant here because the problem statement is pretty dense.”

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Amazon·Machine Learning Engineer·Technical Phone Screen

Jun 2026
Amazon ML engineer interview, technical screen focused entirely on implementing multi-head attention from scratch using only NumPy. No frameworks allowed, no shortcuts. Pretty brutal if your linear algebra is rusty.
  • Implement multi-head scaled dot-product attention from scratch using only NumPy, given input token representations and projection weight matrices for queries, keys, values, and output.

“The core of this is not complicated if you've read the transformer paper, but doing it live under pressure with no framework to lean on is a different story.”

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Amazon·Software Engineer·Online Assessment (OA)

Jun 2026
Amazon SWE coding round, got a grid traversal problem. Pretty standard stuff if you've done any BFS/DFS prep, but the pressure of it being Amazon made me second-guess myself more than I should have.
  • Given a 2D grid of '1's (land) and '0's (water), count the number of islands, where an island is a group of horizontally or vertically connected land cells surrounded by water.

“Classic problem and I knew it, but I still fumbled around for a minute deciding between BFS and DFS before just going with DFS because it felt cleaner to write recursively.”

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Amazon·Software Engineer·Technical Phone Screen

Jun 2026
Amazon SWE interview with a system design flavor, at least for this one question. They asked me to explain how a feed-forward layer works, which felt more ML theory than typical coding round stuff.
  • Can you explain how a feed-forward layer works in a neural network?

“I fumbled the intro a bit because I started talking about backprop before they even asked about training.”

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Amazon·Software Engineer·Onsite - Behavioral / Leadership

Jun 2026
Amazon SWE behavioral round, two questions back to back. Both were the kind where you think you have a good story until you're halfway through it and realize you're not actually answering what they asked.
  • Tell me about a time you went above and beyond for a customer. Walk through the situation, what you specifically did, and how you knew the customer was actually satisfied.
  • Describe a time you solved a complex problem using a simple solution. What other approaches did you consider, why was simplicity the right call, and what did you give up by going that route?

“I had a decent story but fumbled the 'how did you validate delight' part.”

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