Amazon Machine Learning Engineer Machine Learning Interview Questions
Master your tech interview with our curated database of real questions from top companies.
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...
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...
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....
Explain Transformers and MoE in LLMs
You are interviewing for a role working with large language models (LLMs). Explain the following concepts and how they relate to building and scaling ...
Explain XGBoost Parallelism Strategies
Explain How XGBoost Parallelizes Training Scope Describe how XGBoost achieves parallelism: 1. Within a single machine - Histogram-based split findi...
Explain LLM architecture, tuning, evaluation
LLM Architecture, Positional Embeddings, Fine-Tuning (PEFT), Regularization, and Evaluation Context You are interviewing for a Machine Learning Engine...
Explain Collaborative Filtering Approaches
Collaborative Filtering for Recommendations: Approaches, Losses, Regularization, Cold Start, Bias, Evaluation, and Scale Context You are designing a r...
Explain Logistic Regression Fundamentals
Logistic Regression from First Principles Assumptions and Notation - Binary classification with labels y ∈ {0, 1} and features x ∈ R^d. - Linear score...
Explain ML basics: imbalance, metrics, bias-variance
Handling Class Imbalance, Bias–Variance, Metrics, and Model Choices Context You are building a supervised classifier for a highly imbalanced task (e.g...
Explain Layer Normalization in Transformers
Layer Normalization in Transformers: Placement, Gradients, and Practical Trade-offs Task Explain Layer Normalization (LayerNorm) as used in Transforme...
Explain imbalance, metrics, bias-variance, Transformers vs. CNNs
Fraud Detection (≈1% Positives): Imbalance Strategies, Bias–Variance, Metrics, and Model Choices Context: You are building a binary classifier to flag...
Explain key ML concepts and techniques
Onsite Machine Learning Interview: Multi-topic Questions Answer all sections. Be precise and compare alternatives where asked. Favor concrete mechanis...
Explain LLM fundamentals and trade-offs
LLM Fundamentals — Onsite Interview Task Context: Assume a modern transformer-based LLM. Provide precise, concise explanations with examples and trade...