Machine Learning Interview Questions: Complete 2026 Guide

ML interviews have gotten more practical over the past two years. Companies still ask about bias-variance tradeoff and regularization, but the emphasis has shifted toward applied problems: how would you build this model, what features would you use, how would you evaluate it in production.

What ML interviews cover

Based on the 583 ML questions on PracHub, the topics break down roughly like this:

Fundamentals (30-40% of questions)

Bias-variance tradeoff
Overfitting and regularization (L1 vs L2)
Cross-validation strategies
Evaluation metrics (precision, recall, F1, AUC-ROC)
Gradient descent and optimization

Applied ML (25-30%)

Feature engineering for a specific problem
Model selection: when to use what
Handling imbalanced data
Missing data strategies
A/B testing ML models

ML System Design (15-20%)

Design a recommendation system
Design a fraud detection pipeline
Design a search ranking system
Design an ad click prediction system
ML model serving and monitoring

Coding (10-15%)

Implement a simple model from scratch (logistic regression, k-means)
Data manipulation with pandas/numpy
Write a training loop
Feature processing code

Deep Learning (10-15%)

Transformers and attention mechanisms
CNNs vs RNNs vs Transformers
Transfer learning and fine-tuning
LLM-related questions (increasingly common in 2026)

Company-specific focus

Amazon (71 ML questions on PracHub) — Heavy on applied ML. They want to know how you would build a recommendation system for their product pages, how to detect fraudulent reviews, how to optimize delivery routing. Very practical, very business-oriented.

Meta (55 ML questions) — Focuses on content ranking, ads ML, and integrity/safety models. Expect questions about how News Feed ranking works, how to detect harmful content at scale, and how to build a model that balances engagement with user well-being.

Google (36 ML questions) — More theoretical than Amazon or Meta. They will ask you to derive things, explain why certain algorithms work, and discuss mathematical foundations. Also strong on ML infrastructure and model serving.

Questions that keep showing up

These specific questions or close variants appear across multiple companies:

Explain the bias-variance tradeoff. How do you diagnose which one your model suffers from?
When would you use logistic regression over a random forest?
Your model has high AUC-ROC but low precision. What is going on? What do you do?
How would you handle a dataset where 1% of examples are positive?
Design a recommendation system for [specific product]. Walk through the full pipeline.
How do you decide which features to include in your model?
Explain L1 vs L2 regularization. When would you use each?
Your model performs well offline but poorly in production. What could cause this?
How do you A/B test a machine learning model?
Explain how a transformer works. Why has it replaced RNNs for most NLP tasks?

How to prepare

For fundamentals: Make sure you can explain core concepts without a script. The interviewer will ask follow-up questions, and "I memorized the definition" will not hold up. Understand why things work, not just what they are.

For applied ML: Practice end-to-end case studies. Given a business problem, walk through: problem formulation, data collection, feature engineering, model selection, evaluation, deployment, monitoring. This is the round where practical experience shows.

For ML system design: This is similar to regular system design but focused on ML pipelines. Know the components: data ingestion, feature store, training pipeline, model registry, serving infrastructure, monitoring and retraining. Practice drawing these out.

For coding: You will not get LeetCode-hard problems. Expect data manipulation, simple model implementations, and feature processing. Make sure your pandas/numpy skills are solid.

PracHub has 583 ML questions tagged by company, role, and difficulty, plus 225 ML system design questions. The system design questions are particularly useful because they are hard to find elsewhere.