Amazon Machine Learning Engineer Interview Questions
Preparing for Amazon Machine Learning Engineer interview questions means getting ready for a multi-dimensional evaluation: you’ll be assessed on coding and algorithmic problem solving, core machine‑learning theory and applied modeling, ML system design and productionization, plus Amazon’s intense focus on behavioral fit through its Leadership Principles. What’s distinctive about Amazon’s loop is the strong emphasis on building scalable, customer‑obsessed solutions and demonstrating measurable impact; expect at least one ML systems/design conversation that probes data pipelines, feature engineering, model deployment, monitoring, and trade‑offs between latency, cost, and accuracy, alongside coding rounds and a Bar Raiser who evaluates long‑term potential and judgment. For interview preparation, treat this as three parallel tracks: fundamentals (algorithms, statistics, ML concepts), applied engineering (end‑to‑end systems, cloud and data infra, performance and observability), and behavioral storytelling (STAR examples tied to Leadership Principles). Practice whiteboard and online coding problems, rehearse clear explanations of ML projects with metrics and failure modes, and run mock loops that mix technical and behavioral prompts. Prioritize clarity on tradeoffs and customer impact; Amazon rewards candidates who can bridge rigorous technical depth with pragmatic product thinking.
"10 years of experience but never worked at a top company. PracHub's senior-level questions helped me break into FAANG at 35. Age is just a number."
"I was skeptical about the 'real questions' claim, so I put it to the test. I searched for the exact question I got grilled on at my last Meta onsite... and it was right there. Word for word."
"Got a Google recruiter call on Monday, interview on Friday. Crammed PracHub for 4 days. Passed every round. This platform is a miracle worker."
"I've used LC, Glassdoor, and random Discords. Nothing comes close to the accuracy here. The questions are actually current — that's what got me. Felt like I had a cheat sheet during the interview."
"The solution quality is insane. It covers approach, edge cases, time complexity, follow-ups. Nothing else comes close."
"Legit the only resource you need. TC went from 180k -> 350k. Just memorize the top 50 for your target company and you're golden."
"PracHub Premium for one month cost me the price of two coffees a week. It landed me a $280K+ starting offer."
"Literally just signed a $600k offer. I only had 2 weeks to prep, so I focused entirely on the company-tagged lists here. If you're targeting L5+, don't overthink it."
"Coaches and bootcamp prep courses cost around $200-300 but PracHub Premium is actually less than a Netflix subscription. And it landed me a $178K offer."
"I honestly don't know how you guys gather so many real interview questions. It's almost scary. I walked into my Amazon loop and recognized 3 out of 4 problems from your database."
"Discovered PracHub 10 days before my interview. By day 5, I stopped being nervous. By interview day, I was actually excited to show what I knew."
"The search is what sold me. I typed in a really niche DP problem I got asked last year and it actually came up, full breakdown and everything. These guys are clearly updating it constantly."
Explain NLP/RL concepts used in LLM agents
You are interviewing for an applied ML role focused on LLM agents and retrieval-augmented generation (RAG). Answer the following conceptual questions ...
Design a computer-use agent end-to-end
Scenario You are designing a computer-use agent that can complete user tasks on a standard desktop environment by observing the screen and issuing act...
Compute array products excluding self and top-k
Algorithms 1) Product of array except self (no division) Given an integer array nums of length n, return an array ans where: - ans[i] = product of all...
Debug online worse than offline model performance
Production ML: online performance worse than offline You launch an ML model. Offline evaluation (validation/test) looked good, but after deployment th...
Design an S3-like object storage service
Design a cloud object storage service similar to Amazon S3. The service should allow clients to upload, store, and download large files reliably and e...
Handle cold start, dropout, and training stability
Machine Learning deep dive Answer the following conceptual questions (you may use equations and small examples). A) Recommender systems: cold start 1....
Implement PyTorch training loop
Implement a basic PyTorch training loop You are given a PyTorch neural network model, a DataLoader that yields (inputs, targets) batches, an optimizer...
Design a search relevance prediction approach
Search relevance prediction You are asked to predict relevance for an e-commerce search engine (given a user query and a product/document). Prompt 1. ...
Implement K-means and solve interval/frequency tasks
Task 1 — Describe/implement K-means clustering Given: - A data matrix X with shape (n_samples, d). - An integer k (number of clusters). Explain (or wr...
Design systems for global request detection and labeling
Answer the following ML system design questions. State assumptions, propose an architecture, and discuss scaling, latency, and reliability. 1) Global ...
Implement SGD for linear regression and derive gradients
Prompt You are given a dataset of \(n\) 1D samples \(\{(x_i, y_i)\}_{i=1}^n\), where \(x_i\) and \(y_i\) are real numbers. We want to fit a linear mod...
Explain key ML theory and techniques
Onsite Machine Learning Engineer: Mixed Topics You are asked to answer concisely but with depth across the following topics: 1) XGBoost Parallel Compu...
Explain core ML concepts and diagnostics
You are in an ML breadth interview for a Senior Applied Scientist role. Answer the following conceptual questions clearly and practically (definitions...
Test whether two user populations differ
Problem You are given two groups of users: - Group A: North America users - Group B: Europe users Each user has a vector of continuous features (e.g.,...
Design an LLM quality validation system
You are asked to design an end-to-end LLM quality validation system for a team that trains and serves large language models. The goal is to automatica...
Explain ML evaluation, sequence models, and optimizers
Scenario An interviewer is deep-diving into an ML project you built (you can assume it is a supervised model unless specified otherwise). They want yo...
Explain why CTR rises but CVR unchanged
Experiment analysis (CTR up, CVR flat) You run an online experiment on an e-commerce product detail page that launches a new UI. - Primary observation...
Find shortest path in a grid with obstacles
You are given a 2D grid of size m x n representing a maze. Each cell in the grid is either empty (0) or blocked (1). You are also given two coordinate...
List hyperparameter tuning methods
Describe common methods for hyperparameter tuning in machine learning. For each method, explain: - How it works conceptually. - Its advantages and dis...
Design logo infringement detection system
Scenario You work for a large e-commerce company. Brands register their official logos with you (e.g., Nike swoosh, Apple logo, etc.). Third-party sel...