Applied Scientist II applicants have rated the interview process at Amazon with 3.3 out of 5 (where 5 is the highest level of difficulty) and assessed their interview experience as 41% positive. To compare, the company-average is 57.5% positive. This is according to Glassdoor user ratings.
Candidates applying for Applied Scientist II roles take an average of 27 days to get hired, when considering 27 user submitted interviews for this role. To compare, the hiring process at Amazon overall takes an average of 28 days.
Common stages of the interview process at Amazon as a Applied Scientist II according to 27 Glassdoor interviews include:
Phone interview: 29%
Skills test: 21%
One on one interview: 17%
Personality test: 12%
Presentation: 12%
Group panel interview: 6%
Background check: 2%
IQ intelligence test: 2%
Here are the most commonly searched roles for interview reports -
I applied online. The process took 6 months. I interviewed at Amazon (Amsterdam) in Mar 2024
Interview
ask for the computer vision algorithms like ResNet block, how FRCN works, and how yolo works, and what is their difference, after then we did a quick leetcode test (rotate the 2D matrix 90 degrees).
Interview questions [1]
Question 1
ask for the computer vision algorithms like ResNet block, how FRCN works, and how yolo works, and what is their difference, after then we did a quick leetcode test (rotate the 2D matrix 90 degrees).
I applied through a recruiter. I interviewed at Amazon (Seattle, WA) in Jun 2026
Interview
This interview was for the Applied Scientist Position at Amazon. It was a Science breadth/Depth as well as 1 easy LC problem. After that did a full loop interview. 1 Science ML Breadth, 1 Science Depth, 1 Sys Design (Team-specific), 1 coding round and 1 Bar Raiser
Interview questions [1]
Question 1
Bias-Variance tradeoffs. Bagging Vs boosting. Modeling details in my project. Basic Statistical Questions. DSA: String compression problem.
I applied through a recruiter. I interviewed at Amazon (Seattle, WA) in May 2026
Interview
The process started with a technical phone screen, followed by four back-to-back rounds. The first two technical rounds focused on ML breadth and ML depth respectively, while the later rounds focused more on system design, agentic product thinking, and behavioral examples.
Interview questions [1]
Question 1
Design an agentic recommendation system. How would you structure the system, what models or components would you use, and how would you evaluate its quality and safety?
1 HR round
4 technical interviews (coding+depth+breadth) of Machine Learning.
1 round to go into depth of my own projects.
1 round on general data science questions + system design (model a pipeline end-to-end for translating one set of multimodal objects to another language)