Pinterest·Machine Learning Engineer·Technical Phone Screen·Intermediate

Intermediate

Jun 2026

Summary

Interviewed for an ML engineer role at Pinterest and got asked about regularization. Short and focused, nothing too wild.

Questions Asked (1)

Can you explain what regularization is and how it works?

Technical Trade-offs

Author's notes

Bread and butter ML question but I still fumbled the explanation a bit.

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AI HintsAI Generated

Suggested Approach

Start with a clear, concise definition of regularization as a technique to prevent overfitting by adding a penalty for complexity. Then explain the two main types (L1 and L2) and how they work mathematically and intuitively. Finally, discuss practical trade-offs and when to use each, tying it to real-world scenarios like recommendation systems at Pinterest.

Pro tip: Mention that regularization is not just about preventing overfitting but also about improving model generalization and interpretability, and that the choice between L1 and L2 depends on whether you need feature selection or just shrinkage. Also, note that at scale, regularization interacts with optimization and can affect training dynamics.

1. Define regularization

Explain that regularization is a set of techniques used to prevent overfitting by adding a penalty term to the loss function that discourages complex models.

2. Explain how it works

Describe the two main types: L1 (Lasso) adds absolute value of weights, leading to sparsity; L2 (Ridge) adds squared weights, leading to small but non-zero weights. Mention that the penalty is controlled by a hyperparameter lambda.

3. Discuss trade-offs

Compare L1 vs L2: L1 for feature selection, L2 for handling correlated features. Mention that too much regularization can underfit, too little can overfit. Also, note computational considerations at scale.

4. Relate to practical applications

Give examples of how regularization is used in real systems, such as in recommendation models at Pinterest to prevent overfitting on sparse user-item interactions, and how it can be combined with other techniques like dropout.

Key Points to Mention

Definition of regularization and its purpose to reduce variance without increasing bias too much.
L1 regularization (Lasso) promotes sparsity and can be used for feature selection.
L2 regularization (Ridge) shrinks weights and is effective when features are correlated.
The role of the regularization hyperparameter (lambda) and how to tune it (e.g., cross-validation).
Trade-offs: bias-variance trade-off, interpretability vs. performance, and computational cost at scale.
Examples of regularization in practice, such as dropout, early stopping, and data augmentation as other forms of regularization.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.