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Amazon interview notes, page 17 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·Machine Learning Engineer·Onsite - Behavioral / Leadership

Apr 2026
Behavioral round for an ML Engineer role at Amazon. Pretty standard stuff, all leadership principle territory, nothing that should surprise anyone who's done their homework.
  • Tell me about a time you had a conflict with someone and how you resolved it.
  • Describe a time you took ownership of a problem that was outside your usual responsibilities.
  • Walk me through a difficult project and how you dealt with the challenges along the way.
  • What's the biggest mistake or failure you've had, and what did you take away from it?

“I structured my answer as situation-action-result and it felt okay but I rambled a bit on the resolution part.”

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Amazon·Software Engineer·Technical Phone Screen

Apr 2026
Rapid-fire fundamentals round for a Solutions Architect role at Amazon. Four questions back to back, all pretty foundational stuff, though the ETL one came up specifically because of something on my resume which I wasn't fully expecting.
  • Can you explain what DNS is and walk through how name resolution works?
  • What is an IP address? How does IPv4 differ from IPv6, and what's the distinction between public and private addresses?
  • What are the main differences between SQL and NoSQL databases, and how do you decide which to use?
  • What is ETL, and how does it differ from ELT?

“Knew this one cold.”

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Amazon·Software Engineer·Onsite - System Design / Architecture

Apr 2026
Amazon EM interview with a system design question centered on building a leaderboard for a fantasy gaming platform at significant scale. Pretty focused session, one meaty problem, and it went in directions I didn't fully anticipate.
  • Design a leaderboard system for a fantasy gaming platform that supports 100,000 registered teams.

“I jumped straight into storage and ranking logic, which felt right, but I underestimated how much they wanted me to talk about read/write contention at scale.”

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Amazon·Machine Learning Engineer·Technical Phone Screen

Apr 2026
Amazon ML Engineer interview that leaned pretty heavily on algorithm fundamentals. The main focus was a classic top-K problem and they wanted you to walk through multiple solutions, not just code one up.
  • Given an integer array, find the top K largest elements. Walk through different approaches and compare their time and space trade-offs.

“The part that tripped me up was they didn't just want code, they wanted a real comparison.”

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Amazon·Software Engineer·Onsite - System Design / Architecture

Apr 2026
System design round at Amazon for a data engineering role. One question, pretty open-ended, and I spent way too long on the ingestion layer before they nudged me toward the dashboard side of things.
  • Design a data pipeline that refreshes every hour and feeds a dashboard displaying the most frequent Alexa user requests, segmented by country.

“I jumped straight into talking about Kafka and streaming before realizing they said hourly, not real-time.”

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Amazon·Product Manager·Onsite - Product Sense / Strategy

Apr 2026
Amazon PM interview with a product analytics question centered on Netflix as a case study. One question, focused on diagnosing a retention problem for a specific user segment.
  • Netflix sees low retention among users who were activated through watching a series. Why might this be happening, and what would you do about it?

“I went straight to root cause analysis and started listing hypotheses: series-activated users might binge fast and churn once the show ends, or maybe the recommendation engine fails them right after.”

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Amazon·Software Engineer·Onsite - Coding / Algorithms

Apr 2026
Amazon coding round for a software engineer role. One algorithmic problem, fairly involved, left me second-guessing my approach the whole time.
  • Given an integer array, you can pick any two values x and y and replace every occurrence of x with y in one operation. What is the minimum number of such operations to make the array 'contiguous', meaning no two equal values are separated by a different value?

“Took me a while to even understand what 'contiguous' meant in this context.”

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Amazon·Data Scientist·Technical Phone Screen

Apr 2026
Amazon data science loop, one question but it was a monster. They gave you a three-way vendor decision and asked you to build basically an entire decision science framework from scratch, live. Walked out not totally sure if I nailed it or completely missed the point.
  • Your company is choosing between three employee training options: a standard vendor package, a premium vendor package, and an internal build. Design a full quantitative decision framework covering: how you'd define and causally estimate KPIs like completion rate and performance uplift; a risk-adjusted NPV model that accounts for implementation time, costs, benefits, data breach risk, and value lost during rollout; a sensitivity analysis with stopping rules and a pilot plan to update your estimates; and finally, given only ordinal information about the options, which would you pick and what would make you switch?

“This is one of those questions where you realize halfway through your answer that you've been talking for five minutes and haven't gotten to the actual decision rule yet.”

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Amazon·Software Engineer·Technical Phone Screen

Apr 2026
Amazon SWE coding round, got a grid BFS problem about spreading rot through oranges. Pretty classic multi-source BFS setup but the edge cases tripped me up more than I expected.
  • You're given a grid where each cell is either empty, a fresh orange, or a rotten orange. Rotten oranges spread rot to adjacent fresh oranges every minute. Return the minimum number of minutes until no fresh oranges remain, or -1 if it's not possible.

“My first instinct was regular BFS from a single source and I had to catch myself because there can be multiple rotten oranges at the start.”

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Amazon·Machine Learning Engineer·Technical Phone Screen

Apr 2026
Amazon ML engineer technical screen, basically one big coding problem: build a neural net from scratch using only NumPy. No PyTorch, no autograd, nothing. It was a lot to cover in one session and I definitely underestimated how much they'd push on the math.
  • Implement a feed-forward neural network from scratch using only NumPy, including forward pass, backpropagation, cross-entropy loss, and mini-batch SGD. Train it on a small dataset like XOR and be ready to walk through tensor shapes at every step.

“This is the whole interview, basically.”

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