Amazon interview notes, page 114 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 interview with a string decoding problem. Pretty straightforward coding round, nothing too wild, but the implementation details matter more than you'd expect.

Given a Morse code encoded string where single spaces separate letters and a different delimiter (like ' / ') separates words, write a function to decode it back to plain English.

“I built a hardcoded map of Morse patterns to letters, split on the word delimiter first, then split each chunk on single spaces.”

View Post

Amazon·Data Scientist·Technical Phone Screen

Jun 2026

Amazon data scientist interview that was basically a deep dive into one big scenario: build and deploy an ML model that mirrors their internal recommendation pipeline. The whole thing was a single extended question but it had a lot of moving parts and I wasn't fully prepared for how far they'd push on the monitoring side.

Walk me through the feature engineering you did for a recent production model, why you picked that algorithm, what alternatives you considered and rejected, and then describe the full pipeline from raw data ingestion through to online inference and monitoring.

“This is a lot of ground to cover in one question and I kind of fumbled the structure early on.”

View Post

Amazon·Software Engineer·Technical Phone Screen

Jun 2026

Amazon SWE interview with a greedy algorithm problem that looked clean on the surface but had a tricky correctness proof hiding underneath. The kind of question where you can get to a working solution and still feel like you didn't fully nail it.

You have an even number of servers, each with a memory value. Split them into equal-sized primary and backup groups such that every primary is paired with a backup whose memory is at least as large. Maximize the total memory across all chosen primary servers. Walk through a sort-based greedy approach and justify why it's correct.

“My first instinct was right but I couldn't articulate why.”

View Post

Amazon·Software Engineer·Hiring Manager Screen

Jun 2026

Interviewed for a Solution Architect Manager role at Amazon, one question about team scaling. Not much to report in terms of rounds or format, just the one question from what I can tell.

As a Solution Architect Manager, how do you scale your team?

“Talked through hiring strategy, how I think about skill gaps versus headcount, and where I'd lean on process to multiply output without just adding people.”

View Post

Amazon·Software Engineer·Onsite - Coding / Algorithms

Jun 2026

Amazon SWE coding round with a heap simulation problem. The problem looked manageable at first glance but the min-tracking piece is where things get tricky and I think I undersold my complexity analysis.

You have n VM types each with an initial inventory count. For m rentals, each rental picks the type with the highest current stock; the revenue for that rental is the current max stock plus the current non-zero minimum stock. Decrement the chosen type's stock by 1 and repeat. Return the total revenue.

“Started with the brute force scan approach, O(n) per step, which works fine for small inputs.”

View Post

Amazon·Machine Learning Engineer·Technical Phone Screen

Jun 2026

Amazon ML engineer screen, one meaty streaming design problem that took up the whole session. The question had a lot of moving parts and I kept second-guessing my state management approach the whole time.

Design a streaming system that detects 'abusive' book reads from a live event stream. An event is abusive if a user reaches the last 5% of a book before ever completing the first 10%. A book itself is flagged abusive if more than 20% of its total events are abusive. Walk through your data structures, state management, and any concurrency concerns.

“This one took me a while to even parse correctly.”

View Post

Amazon·Software Engineer·Technical Phone Screen

Jun 2026

Amazon SWE coding round, one question, sliding window stuff. Not much to say about the experience itself since the content is pretty thin but the problem was a classic.

Given a string, find the length of the longest substring that contains no repeated characters.

“Classic sliding window problem.”

View Post

Amazon·Data Scientist·Technical Phone Screen

Jun 2026

Amazon data scientist interview that went heavy on regularization theory. Four questions, all interconnected, and the depth expected was not what I anticipated going in.

Write out the objective functions for linear and logistic regression using L0, L1, L2, and L-infinity penalties, in both the constrained form and the penalized form. For each penalty, what coefficient patterns do you expect and how hard is it to optimize?
You have a dataset where two groups of predictors are highly collinear and there are a few outliers in both the features and the target. Which regularization penalty would you choose, and how would you think about elastic net mixing?
Compare linear regression and logistic regression. Write out the model formulas including the link function and the conditional distribution each assumes, and explain when using linear regression for a classification task is actually a problem.
How does feature scaling interact with L1 and L2 regularization, and what goes wrong if you skip it?

“This is where I started sweating.”

View Post

Amazon·Software Engineer·Technical Phone Screen

Jun 2026

SQL question for an Applied Scientist role at Amazon. Pretty standard stuff but worth knowing cold if you're interviewing there.

Write a SQL query to return the second highest distinct salary from an Employee table. If there is no second highest salary, the query should return null.

“Classic question but the null-return requirement is where people slip up.”

View Post

Amazon·Data Scientist·Onsite - Behavioral / Leadership

Jun 2026

Behavioral loop at Amazon for a data scientist role, focused on a single deep-dive question about stepping outside your lane and taking on risk. Pretty intense for what felt like one question.

Tell me about a project where you took a calculated risk that was outside your formal responsibilities. Walk me through the business goal, what obstacles you anticipated, how you evaluated your options, what you did to reduce uncertainty, how you managed the downside, and what the measurable outcome was. Also, what would you do differently, and did you find a way to scale or share the approach?

“This is basically five questions duct-taped together and the interviewer will absolutely follow up on every layer.”

View Post