Amazon interview notes, page 63 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

May 2026

Amazon SWE coding round, one meaty algorithmic problem that took up most of the session. The problem looked like a stack question on the surface but the exact semantics tripped me up at first.

Given an array of integer task priorities where tasks execute right to left, for each index i find the largest index j > i where priorities[j] is strictly less than priorities[i]. The delay time for index i is j - i; if no such j exists, the delay time is 0. Return the full delay time array. You must do better than O(n^2), explain your data structures, analyze time and space complexity, handle duplicates, and handle large inputs.

“The 'rightmost' part is what got me.”

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Amazon·Machine Learning Engineer·Technical Phone Screen

May 2026

Amazon ML engineer screen, one technical question on regression fundamentals. Pretty short and focused, felt more like a warmup than a deep dive.

What are the key differences between linear and logistic regression?

“Knew this cold but still fumbled the output layer explanation a bit.”

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Amazon·Software Engineer·Onsite - Behavioral / Leadership

May 2026

Amazon behavioral round for a software engineering role, centered on a classic deadline pressure question. The emphasis on trade-off reasoning rather than hustle caught me a bit flat-footed the first time I thought through my answer.

Tell me about a time you had to deliver something under a tight, non-negotiable deadline. Walk me through the situation, what you committed to, the trade-offs you made, and what actually shipped.

“The part I almost fumbled was the trade-off section.”

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Amazon·Product Manager·Onsite - Behavioral / Leadership

May 2026

Interviewed for a PM role at Amazon. One behavioral question, pretty standard for their leadership principles loop.

Describe a situation where you had to dig past the surface to find the actual root cause of a problem.

“Classic Amazon behavioral territory.”

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Amazon·Software Engineer·Technical Phone Screen

May 2026

Amazon SWE coding round, just the one question but it was a classic expression parsing problem that I thought I knew better than I did.

Implement a basic calculator that can evaluate arithmetic expressions with +, -, *, and / operators on integer operands, without using built-in eval functions.

“I've seen this problem before so I thought I'd breeze through it.”

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Amazon·Machine Learning Engineer·Technical Phone Screen

May 2026

Amazon ML Engineer interview that went deep on bandit algorithms, way deeper than I expected. They weren't satisfied with just naming the algorithms, they pushed hard on tradeoffs, production edge cases, and things like delayed rewards that I hadn't drilled enough.

What are multi-armed bandit algorithms, and when would you choose one over a traditional A/B test?
Compare epsilon-greedy, UCB, and Thompson sampling. How do they differ in terms of regret, exploration-exploitation balance, and underlying assumptions?
How would you extend standard bandit approaches to contextual bandits, and what changes in terms of modeling and complexity?
How do you handle delayed or batched rewards in a bandit system?
How do you detect and handle non-stationarity or distribution drift in a deployed bandit?
How would you do offline policy evaluation for a bandit before deploying it to production?
What safety mechanisms or guardrails would you put around a bandit system running in production?

“Felt pretty solid here.”

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Amazon·Software Engineer·Online Assessment (OA)

May 2026

Amazon SWE online assessment with a string manipulation problem involving simulated nucleotide removal. Pretty niche premise but underneath it's just a simulation/greedy problem once you strip away the biology framing.

Given a string representing a genome sequence and a character representing a mutated nucleotide, find the earliest time at which no more removals can occur. The mutated nucleotide removes adjacent nucleotides to its left one at a time per unit of time.

“The biology wrapper threw me off for a second.”

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Amazon·Product Manager·Onsite - Behavioral / Leadership

May 2026

Amazon PM behavioral round, pretty much what you'd expect. One question, standard leadership principles territory, nothing shocking but also not easy to nail on the spot.

Describe a situation where you applied analytical thinking to solve a complicated problem in an unconventional way.

“I had a decent story ready but fumbled the 'creative' angle.”

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Amazon·Software Engineer·Onsite - Coding / Algorithms

May 2026

Amazon SWE coding round, basically just a grid BFS problem. Pretty standard stuff but the pressure of getting the edge cases right in real time is a different beast.

Given a 2D binary grid where 1 is walkable and 0 is blocked, find the minimum number of steps to travel from the top-left corner to the bottom-right corner using 4-directional movement. Return -1 if no path exists.

“Classic BFS shortest path on a grid.”

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Amazon·Software Engineer·Onsite - Multi Round

May 2026

Amazon SWE loop, every round had a behavioral component tacked on. The coding portion wasn't a typical algorithm grind. they threw a statistics-over-streaming-data problem at me which I wasn't expecting at all.

Given a continuous stream of incoming data, how would you compute and maintain statistical measures (such as mean, variance, or percentiles) efficiently?

“Not what I prepped for.”

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