Amazon Machine Learning Engineer Interview Questions
46+ questions from real Amazon Machine Learning Engineer interviews, reported by candidates.
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Round 1(OA Assessment - 2 hrs):It was conducted in HackerEarth.3 Programming Questions were askedRound 2(Technical Interview - 1 HR)Self IntroDirectly stepped into coding ...
Nned direction for future prepration.
I’m currently working as a Machine Learning Engineer with \~3 years of experience in a service-based company, and I’m planning to switch to a product-based company (targeting companies like Amazon, Ub
Amazon SDE I Intern OA Cleared but Recruiter Is Aligning Me With ML/AR Teams — Is This Normal?
Hi everyone, I completed the Amazon SDE Summer 2026 (USA) Internship Online Assessment months ago and just now received an email from a recruiter saying that I successfully cleared the OA. In the emai
This post was last edited by Anonymous on 2025-09-25 12:27 ## Telephone (Interviewer: Senior RS): The first half hour was about projects, similar to system design questions: Why design this product? W
Amazon Applied Scientist GenAI Interview Experience and Insights
For the applied scientist position in Amazon's GenAI area, the process started with a technical phone screen. After passing that, there were four virtual interviews (VOs): one in ML depth, one in ML b
Phone screen 1: Implement AUC calculation and some follow-up questions, such as underfitting and overfitting. Phone screen 2: LeetCode merging cross intervals, and some questions about MOE (Mean Excha
**Background:** - 3+ YOE as DS/MLE in a Product Based Company - B.Tech from IIT (non-circuital) **Screening Round (Eliminative)** - 1 DSA question - 1 probability question - ML fundamentals - Question
I've been reading almost all the posts from this sub-reddit for past few weeks (since the day I got my interview loop invite). Kinda wanted to give something valuable back to the community. I wanna st
Hello everyone, I want to provide my experience with **Amazon** **Applied Scientist** interview. I took a lot from this subreddit and similar communities and want to give back. I hope this will help s
Amazn AS phone screen experience/chances.
Had the phone screen for AS today. Where I did well: Classical ML questions: Answered all correctly Leetcode: It was a question I read this morning. Solved it in 5 mins. Was asked to code up brute for
I have been to couple of interviews and the interviewer wanted to know whether I have experience on deploying AI/ML models, whether I have used SageMaker etc. Though I know the concepts, not really us
Status: M-Tech CSE student, old IITs Position: Applied scientist intern (6 months) at Amazon Location: Bengaluru (in person) Date: Dec 1st, 2023 (whole process after application took 2-3 weeks) Application: - Applied through ML...
Amazon | Applied Scientist | India | Feb 2024
Coding Concepts (1hr): Problem 1: https://leetcode.com/problems/first-missing-positive/description/ **Problem 2: There is a sale coming on Amazon.in, so sellers are preparing their inventory for the sale, all the sellers putting prices of every item they have....
Amazon Applied Scientist Coding Round
You are given the following conversion factors: """ kilogram gram 1000 kilogram pound 2.21 feet inch 12 ... """ Now, given from_unit and to_unit, output the conversion factor and if it isn\'t possible raise an error. Ex:...
Got this question in OA for Amazon ML summer school, India. 2024 Sam\'s Dilemma Sam is solving an interesting math problem. He is working with integer arrays and trying to perform some...
Very poor conduct of Online Assessments by Amazon ML summer school Hello everyone !! I request everyone to please read the article completely and express your views on it. On 23-06-2024...
Amazon Early Career SDE position Interview Experience
I recently completed a series of three virtual onsite interviews for an SDE-1 (AI/ML) position at Amazon. - 1st Round: Focused on a LeetCode medium-level question involving the greedy + two-pointer...
I’m a final-year BTech student passionate about Artificial Intelligence (AI) and Machine Learning (ML). This year, I had the privilege of being part of Amazon ML Summer Sc...
I had the opportunity to participate in the Amazon ML Summer School after being selected through an application process. The selection involved an aptitude test and an OA ...
Round 1 (Online Assessment)The initial assessment comprised 50 MCQs covering various topics including aptitude, logical reasoning, and technical concepts. The questions we...
What Amazon Looks for in Machine Learning Engineer Interviews
Amazon Machine Learning Engineer interviews are calibrated against the level and scope expected of the role. Across 46+ verified candidate reports on LeakCode, the consistent signals interviewers look for: clear problem decomposition before coding, explicit complexity reasoning, structured handling of edge cases, and the ability to articulate trade-offs between two reasonable approaches.
The discriminator between candidates who advance and candidates who do not is rarely the final correctness of the solution. It is the path to the solution: did you ask clarifying questions, did you state your approach before coding, did you handle edge cases without prompting, and did you communicate your reasoning throughout. Reports tagged "no hire" frequently cite a working solution with poor communication; reports tagged "strong hire" cite clear thinking even when the final solution was incomplete.
How To Use This Question Set
Real interview reports are a calibration tool, not a memorization target. Companies update their question pools every 2-4 months; memorizing exact problems risks misleading you when the interviewer uses a variant. The high-leverage use: identify the patterns that appear repeatedly in Amazon Machine Learning Engineer reports, practice those patterns on similar (not identical) problems, and use the reports to understand the interviewer's typical follow-up depth.
Filter the questions below by round type, difficulty, and recency. Focus first on reports from the past 6-12 months; older reports may reference questions that have since rotated out of Amazon's pool. Reports tagged with quantified difficulty (e.g., "medium-hard") are higher-signal than reports without difficulty tags.
Round-by-Round Expectations
Amazon Machine Learning Engineer loops typically span 4-6 rounds across phone screens and on-site or virtual on-site interviews. The structure varies by company: some run 1 recruiter screen + 1 technical phone + 3-4 on-site rounds; others run 1 recruiter screen + 1 OA + 4-5 on-site rounds. The recruiter screen is logistics and culture-light; the technical phone screen is medium-difficulty coding; the on-site loop covers coding, system design (at L4+ levels), and behavioral rounds.
Each round is designed to surface a specific signal. Coding rounds: correctness, code quality, complexity reasoning, communication. System design rounds: requirements clarification, design judgment, operational thinking. Behavioral rounds: ownership scope, leadership, ambiguity tolerance, conflict navigation. Strong candidates explicitly hit each signal dimension out loud during the round; weak candidates focus only on solving the prompt.
Common Interview Mistakes At This Combination
Reports tagged "no hire" at Amazon Machine Learning Engineer commonly cite: jumping into code without clarifying requirements, coding silently for 10+ minutes without verbalizing approach, missing edge cases (empty input, single element, very large input, overflow), and producing a working solution that the candidate cannot explain or refactor when probed. Strong candidates avoid these patterns by following a consistent template: clarify, verbalize approach, code with narration, test with examples.
Behavioral and design rounds have their own failure modes. Behavioral: stories that use "we" instead of "I" diluting individual signal, stories with no quantified outcome, defensiveness when probed about failure. Design: not asking clarifying questions, not stating requirements out loud, designing for a single server when the prompt clearly implies scale, ignoring operational concerns (deployment, monitoring, rollback). These show up in roughly half of Amazon Machine Learning Engineer interview retrospectives on LeakCode.
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