Pinterest·Machine Learning Engineer·Technical Phone Screen·Intermediate

Intermediate

Jun 2026

Summary

Pinterest ML engineer interview with a fill-in-the-blank coding format, which I wasn't expecting at all. They gave you a template and you had to implement Naive Bayes from scratch, computing priors, posteriors, the whole thing. NumPy was fair game at least.

Questions Asked (1)

Given a code template with blanks, implement Naive Bayes classification including computing the prior and posterior probabilities.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

The fill-in-the-blank format threw me off more than the actual math did.

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AI HintsAI Generated

Suggested Approach

First, clarify the problem and the template structure, then outline the Naive Bayes algorithm step by step, focusing on how to compute priors and posteriors. Implement the solution by filling in the blanks, ensuring correct handling of probabilities and edge cases.

Pro tip: Demonstrate awareness of numerical stability by using log probabilities to avoid underflow, and mention how to handle unseen features with Laplace smoothing.

1. Understand the Template and Problem

Review the code template to identify the blanks and understand the expected inputs and outputs. Clarify any ambiguities about the dataset or assumptions.

2. Compute Prior Probabilities

Calculate the prior probability for each class by counting the frequency of each class in the training data and dividing by the total number of instances.

3. Compute Likelihoods with Smoothing

For each feature and class, compute the conditional probability of the feature given the class, applying Laplace smoothing to handle zero counts.

4. Compute Posterior Probabilities

For a given instance, compute the posterior probability for each class by multiplying the prior with the likelihoods of each feature (or summing logs). Normalize if needed.

5. Predict and Validate

Predict the class with the highest posterior probability. Test the implementation with simple cases to ensure correctness and discuss potential improvements.

Key Points to Mention

Naive Bayes assumption of conditional independence between features.
Calculation of prior probabilities from class frequencies.
Use of Laplace smoothing to handle unseen feature values.
Numerical stability: using log probabilities to prevent underflow.
Handling of categorical vs. continuous features (e.g., Gaussian Naive Bayes for continuous).
Evaluation metrics for classification (e.g., accuracy, precision, recall).

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