Machine Learning Interview Guide

Machine Learning Interview Guide: ML Engineer and Research Roles

ML interviews test both theoretical understanding and practical implementation. Google, Meta, Apple, and OpenAI evaluate: ML fundamentals (bias-variance, regularization, cross-validation), system design for ML (feature stores, model serving, A/B testing), and coding (implementing algorithms from scratch).

The three pillars of ML interviews: theoretical foundations (statistics, optimization), practical skills (feature engineering, model evaluation, debugging), and ML system design (training pipelines, serving infrastructure, monitoring).

Core ML Topics

• Supervised Learning — Linear regression, logistic regression, decision trees, random forests, gradient boosting, neural networks
• Unsupervised Learning — K-means, DBSCAN, PCA, autoencoders
• Deep Learning — CNNs (vision), RNNs/Transformers (NLP), GANs (generation)
• ML Systems — Feature stores, training pipelines, model serving, A/B testing, monitoring drift
• LLMs — Prompt engineering, fine-tuning, RAG, evaluation metrics

For AI/ML-specific roles: AI/ML engineer 2026 guide. Related: data science interviews.