Machine Learning Engineer · Full Journey · Multiple Types

Pinterest — Machine Learning Engineer ❌ Failed

Level: Mid-Level

Round: Full Journey · Type: Multiple Types · Difficulty: 6/10 · Duration: 120 min · Interviewer: Neutral

Topics: Machine Learning, Overfitting, Regularization, System Design, CTR Prediction, Embeddings, Recommendation Systems

Location: San Francisco Bay Area

Interview date: 2025-12-01

Got offer: False

Summary

Round 1: Technical Screen

Question: Rapid-fire questions on machine learning basics: What is overfitting? How do you prevent it? What are the differences between L1 and L2 regularization?

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Round 2: ML Practitioner

Question: Design a system to improve the click-through rate (CTR) of ads for an online social media platform. The interviewer asked in detail about how to build embeddings (referencing PinSage and PinnerSage) and how to obtain training data. I answered using user history logs, but it seemed incorrect.

Details

Round 1: Technical Screen

The interviewer asked a series of quick-fire questions on machine learning fundamentals. I needed to demonstrate a solid understanding of overfitting and regularization techniques.

Topics covered:

• Overfitting: Definition and impact.
• Overfitting Prevention: Techniques to mitigate overfitting.
• L1 vs. L2 Regularization: Differences in their approaches and when to use them.

Round 2: ML Practitioner

The core of this round was a system design question focusing on improving ad CTR.

The task: Design a system to optimize the click-through rate of ads on a social media platform.

Key discussion points:

• Embedding Creation: How to build effective embeddings for users and ads (with reference to PinSage and PinnerSage).
• Training Data: The interviewer pressed on the source of training data. My suggestion of using user history logs might not have been the ideal answer.

Preparation Tips & Key Takeaways

What I Learned

• The importance of thoroughly understanding the different sources of training data and their potential biases.
• I should have researched more about advanced embedding techniques used in recommendation systems.

Recommended Preparation

Machine Learning Concepts

• Review the fundamentals of overfitting and regularization.
• Deeply understand various machine learning models and their applications.

System Design

• Study common system design patterns for recommendation systems and ad tech.
• Focus on different strategies for building embeddings and collecting training data.

Resources I Recommend

• Research papers on PinSage and PinnerSage.
• Online courses and articles about ad CTR prediction and recommendation systems.

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