Machine Learning Engineer Machine Learning 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...
Build and evaluate click prediction models
Click-Through Rate (CTR) Prediction: Build, Compare, and Justify Models Context You are given a tabular dataset for binary click prediction (click = 1...
Explain bias-variance and evaluate a classifier
You are interviewing for an Applied Scientist internship. Answer the following ML foundations questions. 1) Bias–variance - Define bias and variance i...
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.,...
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...
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 learning-rate fluctuation and vanishing gradients
ML Fundamentals Answer the following conceptual questions: 1. Learning rate vs. training stability: Why can training metrics (loss/accuracy) fluctuate...
Explain your VLM project end-to-end
You are asked to deep-dive (“resume grilling”) on a Vision-Language Model (VLM) project listed on your resume. Cover the following clearly and concret...
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...
Train and improve a scikit-learn binary classifier
Practical ML fundamentals (Python + scikit-learn) You are given a small toy binary-classification dataset (e.g., arrays/dataframes X_train, y_train, X...
Explain core ML concepts and lifecycle
You are interviewing for an ML Engineer role. Answer the following (conceptually; no code required): 1) Bias–variance tradeoff - What are bias and var...
Explain overfitting and how to prevent it
You are asked rapid-fire ML fundamentals questions. 1. What is overfitting? Explain it in terms of training vs. validation performance and generalizat...
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 GRPO-style training for diffusion models
You are given a pretrained image diffusion model that generates images conditioned on text prompts (e.g., a text-to-image model). You now want to fine...
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...