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Amazon interview notes, page 41 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·Product Manager·Technical Phone Screen

May 2026
Amazon interview with a classic estimation question. Nothing too wild, but these always make me second-guess my assumptions mid-answer.
  • Estimate the total number of videos currently on YouTube.

“I started with upload rate per minute, which felt right, then tried to back into total volume by estimating how long YouTube has existed and how upload rates have grown over time.”

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Amazon·Software Engineer·Onsite - Behavioral / Leadership

May 2026
Behavioral loop at Amazon for an Applied Scientist role. One question but it had a lot of moving parts and I don't think I handled the uncertainty framing as well as I could have.
  • Tell me about a time you had to make a trade-off decision without enough information. How did you figure out what you could know versus what you couldn't, what assumptions did you surface explicitly, how did you reduce the risk of being wrong, and how did you communicate that uncertainty to stakeholders?

“This question is deceptively heavy.”

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Amazon·Software Engineer·Technical Phone Screen

May 2026
Amazon SWE coding round, one problem the whole session. Grid traversal, which sounds straightforward until you actually have to think about edge cases under pressure with someone watching you.
  • Given a 2D grid of elevations and a starting cell, find all cells that water can reach by flowing only to strictly lower neighbors in 4 directions.

“BFS or DFS from the start, only expanding to neighbors with strictly lower height.”

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Amazon·Software Engineer·Technical Phone Screen

May 2026
Applied Scientist loop at Amazon, and this one question basically covered the entire theoretical backbone of deep learning in one shot. It was a lot to unpack in real time.
  • Walk me through the mathematical foundations of training neural networks, covering loss functions, gradient descent variants, and backpropagation.

“This was basically three questions stapled together and I didn't realize it until I was already two minutes into explaining MSE vs cross-entropy.”

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Amazon·Product Manager·Onsite - Behavioral / Leadership

May 2026
Went through a behavioral round for a PM role at Amazon. Just one question but it was the kind that sounds straightforward until you're actually in it trying to remember a story that doesn't make you look incompetent.
  • Describe a situation where you had to find a simple solution to a genuinely complex problem.

“I blanked for a second because every example I had in my head either wasn't that complex or the solution wasn't really that simple.”

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Amazon·Software Engineer·Online Assessment (OA)

May 2026
Amazon SWE online assessment, one coding problem about greedy placement of security units across cities. Pretty clean problem statement but the edge cases took me longer than I'd like to admit.
  • You have n cities in a row, each with a population value and a binary string indicating whether a security unit is present. Each unit can optionally move one step to the left. A city is protected if it ends up with at least one unit. Find the maximum total population of protected cities.

“Spent the first few minutes just re-reading the problem because I wasn't sure if units could stack or if each city just needed one.”

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Amazon·Product Manager·Onsite - Behavioral / Leadership

May 2026
PM behavioral round at Amazon, just the one question but it required a pretty specific kind of answer.
  • Tell me about something you learned that improved your job performance.

“I blanked for a second because it sounds easy but then you realize you need an actual concrete example, not just 'I learned to communicate better.' I ended up talking about picking up some basic SQL to pull my own data instead of waiting on analysts, which felt relevant.”

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Amazon·Product Manager·Onsite - Product Sense / Strategy

May 2026
Product sense interview at Amazon, just one question but it's the kind that spirals fast if you're not careful with your structure.
  • How many 'likes' happen on Instagram each day?

“Classic estimation question and I still fumbled the setup.”

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Amazon·Software Engineer·Onsite - Behavioral / Leadership

May 2026
Behavioral round at Amazon for a software engineer role. One question, but it had a lot of layers to it and I felt like I was juggling four different answers at once.
  • Tell me about a time you had to work under a tight deadline. How did you decide what to prioritize, what trade-offs did you make, what did you cut, and how did you keep stakeholders in the loop?

“This felt like four questions stitched together and I kind of answered them out of order.”

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Amazon·Software Engineer·Onsite - System Design / Architecture

May 2026
Amazon robotics intern loop, supposedly a standard coding round but turned into a full architecture design session on warehouse robot human-avoidance. The recruiter screen promised behavioral plus LeetCode and delivered something completely different, so heads up if you're prepping for this slot.
  • How would you design a warehouse robot to detect and avoid humans? Walk through your sensor choices, data structures, and decision loop.
  • What happens if the depth camera fails mid-run? How does your system respond?
  • How would you evaluate offline whether your human-avoidance policy actually works?

“This is the whole interview, basically.”

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