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Grokking the Machine Learning System Design Interview

Ad CTR Prediction: Data Strategy and Feature Engineering

Understand how to design effective data strategies and feature engineering for ad click-through rate prediction systems. Learn to categorize features into user, ad, context, and cross features, handle high-cardinality sparse data with embeddings and compression, and implement a real-time versus pre-computed feature serving architecture that meets strict latency requirements. Gain practical skills to estimate storage and query throughput, essential for scalable ML system design interviews.

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In a MAANG system design interview, proposing a transformer-based ranking model for ad CTR prediction earns you a nod. But the follow-up question, “How do you serve user features for a billion users under 20 milliseconds?” is where most candidates stumble. The model architecture is only as good as the features it consumes, and in CTR prediction, feature engineering is the design problem that separates L4 answers from Staff-level ones.

The previous lesson established the eCPM equation (eCPM=pCTR×bideCPM = pCTR \times bid), defined business metrics, and locked in the sub-100ms end-to-end latency constraint. With that foundation set, the focus now shifts to the raw material powering pCTR: features. The CTR model must capture who the user is, what the ad is, when and where the impression occurs, and how these dimensions interact, all within a 10–20ms inference window. Four feature families organize this complexity: user, ad, context, and cross features. This lesson defines each family, tackles the high-cardinality categorical challenge with embeddings, designs the real-time vs. pre-computed feature split, and works through back-of-the-envelope estimates for storage and QPS.

Attention: A candidate who proposes a sophisticated model architecture but hand-waves feature design will fail at L5+ rounds. Interviewers probe how features are sourced, stored, and served under latency constraints, not just what the model looks like.

The four feature families

CTR prediction draws signal from four distinct families, each capturing a different dimension of the ad impression event. Think of it like a restaurant recommendation: you need to know the diner’s preferences (user), the dish being offered (ad), the time and setting of the meal (context), and whether this particular diner has enjoyed similar dishes before (cross features).

The following families form the organizing taxonomy for every CTR feature engineering discussion:

  • User features: These encode who is seeing the ad. Demographic attributes like age bucket, country, and device type provide static context. Behavioral signals such as historical CTR, category affinity scores, and session depth capture engagement patterns. Long-term signals like 30-day click-through rates on specific ad categories reveal stable preferences.

  • Ad features: These describe what is being shown. Creative metadata ...