Amazon Machine Learning Engineer Interview Questions
Practice the exact questions companies are asking right now.
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 ML statistics and model design concepts
Technical Phone Screen: Theory + System Design Probability and Statistics 1. Define a moment generating function (MGF) and explain how it is used. 2. ...
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 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.,...
Find shortest transformation steps in a word graph
You are given two strings begin and end of the same length, and a list words of distinct strings (also same length). You can transform one string into...
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...
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 parallelism and collectives in training
Parallelism strategies and communication in large-scale training You are designing a distributed training setup for very large neural networks that ca...
Compare float types and design ablation
Floating-point types and ablation study design You are training deep neural networks on modern accelerators that support multiple floating-point forma...
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...
Explain weight initialization methods and goals
Explain why weight initialization matters in deep neural networks. Then describe common initialization methods (such as random normal/uniform, Xavier/...
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...
Contrast CNNs and fully connected networks
Compare convolutional neural networks (CNNs) with fully connected (dense) networks. Explain: - The structural differences between convolutional layers...
Analyze attention complexity and improvements
In the context of Transformer-style models, analyze the computational complexity of self-attention. Assume a sequence length of \(n\) and hidden dimen...
Compare decision trees and random forests
Compare decision trees and random forests. In your answer, discuss: - How a single decision tree is built and its main advantages and disadvantages. -...
Explain vanishing gradients and activations
Explain the vanishing gradient problem in deep neural networks. In your answer: - Describe how backpropagation works at a high level and why gradients...
Describe overfitting and L1/L2 regularization
Define overfitting in machine learning and explain why it is harmful. Then describe L1 and L2 regularization: - How each one modifies the loss functio...
Explain the bias–variance trade-off
Explain the bias–variance trade-off in supervised learning. In your answer, cover: - What bias and variance mean in the context of a prediction model....
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...