Pinterest·Machine Learning Engineer·Technical Phone Screen·Intermediate

Intermediate

Jun 2026

Summary

Pinterest MLE screen, at least part of it was multiple choice on fundamentals. Nothing too wild but they clearly want you to know your metrics cold.

Questions Asked (1)

Define precision and recall. What are their formulas, and when would you prioritize one over the other?

Product Analytics & MetricsTechnical Trade-offs

Author's notes

Came up as multiple choice which felt almost too easy, but the follow-up logic is where you can actually fumble.

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AI HintsAI Generated

Suggested Approach

Start by clearly defining precision and recall, including their formulas, and then explain the trade-off between them. Use a concrete example to illustrate when to prioritize one over the other, and tie it back to the business context at Pinterest.

Pro tip: Demonstrate maturity by discussing the F1 score as a balanced metric and mentioning that the choice between precision and recall should align with the product's goals and user experience.

1. Define Precision and Recall

Precision measures the accuracy of positive predictions, while recall measures the ability to find all positive instances. Provide clear definitions with examples.

2. Present the Formulas

State the formulas: Precision = TP / (TP + FP), Recall = TP / (TP + FN). Explain each term briefly.

3. Explain the Trade-off

Discuss how improving one often reduces the other, and introduce the precision-recall curve and F1 score as tools to balance them.

4. When to Prioritize Each

Give scenarios: prioritize precision when false positives are costly (e.g., spam detection), and recall when false negatives are costly (e.g., disease detection).

5. Relate to Pinterest Context

Connect to Pinterest: e.g., prioritize precision for search results to avoid irrelevant pins, and recall for content recommendations to ensure diverse and comprehensive suggestions.

Key Points to Mention

Definitions of precision and recall with formulas
The trade-off between precision and recall
F1 score as a harmonic mean of precision and recall
Examples of when to prioritize precision (e.g., spam filtering, search relevance)
Examples of when to prioritize recall (e.g., medical diagnosis, content recommendation)
Business impact and user experience considerations at Pinterest

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