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Amazon interview notes, page 93 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, just one question but it had a follow-up that tripped me up a bit. Permutations are classic but the circular twist is where things got interesting.
  • Given N students standing in a line, print all possible arrangements. Follow-up: what if the students are arranged in a circle instead?

“The base case felt fine, standard backtracking, swap and recurse.”

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Amazon·Machine Learning Engineer·Technical Phone Screen

Jun 2026
Amazon ML engineer interview with a coding round that leaned more algorithmic than I expected. The main problem was a classic array manipulation question but the follow-ups pushed into territory I hadn't fully prepped for.
  • Given an integer array, return a new array where each element is the product of all other elements in the original array. You cannot use division, and must do it in O(1) extra space (not counting the output array).
  • Follow-up: how does your solution behave if the array contains many zeros? Walk through the edge cases.
  • Follow-up: what if the numbers are very large and risk integer overflow? Discuss using logarithms and exponentiation as an alternative approach, and explain the precision trade-offs involved.

“I knew the prefix/suffix product trick so the base case wasn't the problem.”

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Amazon·Product Manager·Onsite - Behavioral / Leadership

Jun 2026
PM loop at Amazon, behavioral focus. One question but it's the kind that can go sideways fast if you're not careful about how you frame the story.
  • How would you resolve a disagreement with a colleague?

“I went straight into a conflict story without setting up why the disagreement mattered, and I could tell the interviewer wanted more stakes.”

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Amazon·Software Engineer·Technical Phone Screen

Jun 2026
Amazon SWE coding round, one problem the whole session. Classic dynamic programming territory but with a twist on the output format that tripped me up a bit.
  • Given a string and a dictionary of words, determine if the string can be segmented into a sequence of dictionary words (reuse allowed). If yes, return one valid decomposition as an ordered list; if not, return an empty list. Discuss time and space complexity for large inputs.

“I knew word break immediately, done it before.”

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Amazon·Software Engineer·Technical Phone Screen

Jun 2026
Amazon SWE coding round where I got a LeetCode problem in the 300s range, started with brute force, then got asked to redo it with a heap. Ran out of time before finishing the heap version.
  • Solve a LeetCode-style problem (300s range) first with a brute-force approach, then optimize it using a heap.

“Got the brute force down fine, felt decent about it.”

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Amazon·Data Scientist·Technical Phone Screen

Jun 2026
Amazon Data Scientist interview that was basically a deep dive into LLM system design. The whole session revolved around one big scenario: building a client chatbot and figuring out the right architecture. More technical than I expected for a DS role, felt closer to an MLE interview.
  • How would you decide between fine-tuning and RAG when building an LLM application for a client?
  • Walk through the different fine-tuning approaches including full fine-tuning, instruction tuning, LoRA, and embedding fine-tuning, and compare them.
  • Explain how LoRA works mechanically and what advantages it gives you at inference time.
  • If the documents your RAG system retrieves are consistently low relevance, what would you do to improve retrieval quality?
  • If the embedding model itself is the retrieval bottleneck, how would you fine-tune it? What data do you need and what does the training process look like?
  • How would you design a chatbot architecture that can handle questions spanning multiple knowledge domains?

“This was the anchor question for the whole interview.”

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Amazon·Software Engineer·Online Assessment (OA)

Jun 2026
Amazon SDE 2 coding question involving sticks and rectangles. Pretty much a greedy/sorting problem dressed up in game flavor text, which honestly took me a second to cut through.
  • Given an array of stick lengths, find the maximum total area of rectangles you can form, where each rectangle needs two pairs of equal-length sticks and you can shorten any stick by at most 1. Return the result modulo 10^9 + 7.

“The game framing slowed me down more than the actual problem did.”

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Amazon·Software Engineer·Hiring Manager Screen

Jun 2026
Amazon interview, one question about how you'd approach your first day on the job. Not much to go on from the content itself.
  • If today were your first day at the company, what would you want to start working on?

“Classic 'show me you care about impact' question.”

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Amazon·Software Engineer·Online Assessment (OA)

Jun 2026
Took the Amazon SDE intern online assessment, which had two coding problems plus a work simulation and work styles section. Coding went well, the behavioral stuff felt fine but hard to gauge.
  • Two DSA-style coding problems as part of the online assessment.
  • Work simulation and work styles questions tied to leadership principles.

“Aced both with full test case scores, so no complaints there.”

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Amazon·Machine Learning Engineer·Technical Phone Screen

Jun 2026
Amazon ML Engineer phone screen focused entirely on NLP fundamentals, specifically the math and intuition behind surprisal. Pretty niche topic for an interview but if you've done any language modeling work you'll recognize it immediately.
  • How do you define surprisal for a token or event with probability p?
  • What units can surprisal be expressed in, and what determines which unit you're using?
  • Can you explain how surprisal connects to cross-entropy and perplexity in the context of language modeling?
  • What are common mistakes or misunderstandings people make when interpreting surprisal values?

“Straightforward if you know it: negative log of p.”

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