Machine Learning Interview Questions

Topic Guide

Machine Learning Interview Questions

Machine learning interviews test your understanding of algorithms, model evaluation, feature engineering, and deployment considerations. With the rise of LLMs and generative AI, these interviews now increasingly cover transformer architectures, RAG systems, and ML system design. Companies like Anthropic, OpenAI, Google, and Meta heavily weight ML knowledge for research and engineering roles.

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All Machine Learning Interview Questions

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Frequently Asked Questions

What is the difference between overfitting and underfitting?
Overfitting occurs when a model learns noise in training data and performs poorly on unseen data. Underfitting means the model is too simple to capture patterns. Address overfitting with regularization, cross-validation, and more data. Fix underfitting with a more complex model or better features.
How do you handle class imbalance?
Common approaches include oversampling (SMOTE), undersampling, class weights in the loss function, choosing better metrics (F1, AUC-ROC instead of accuracy), and ensemble methods designed for imbalanced data.
What are transformers and why are they important?
Transformers are neural architectures based on self-attention that process all tokens in parallel. They are the foundation of modern LLMs. Their ability to capture long-range dependencies and parallelize training made them far more effective than RNNs and LSTMs.
What is RAG and when would you use it?
RAG (Retrieval-Augmented Generation) combines retrieval with generation. Instead of relying solely on LLM knowledge, RAG retrieves relevant documents from an external knowledge base as context. Use it when you need factual accuracy, domain-specific knowledge, or up-to-date information.

What Interviewers Look For

Interviewers evaluate both theoretical depth and practical judgment. They want to see that you can select appropriate models, diagnose issues like overfitting or data leakage, design training pipelines, and reason about trade-offs between accuracy, latency, and cost. For ML system design, structured thinking about the end-to-end pipeline matters more than memorized formulas.