How to Architect a Personalized Ads Serving System
Overview
This question evaluates a data scientist's competency in designing end-to-end personalized advertising systems within the Machine Learning domain, covering system-level ML architecture, feature engineering and stores, multi-stage model pipelines, real-time serving, experimentation, and evaluation under latency and scale constraints.
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Aug 4, 2025, 10:55 AM
Data Scientist
Technical Screen
Machine Learning
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Full-Funnel Ads Serving System Design
Scenario
You are asked to architect a full-funnel advertising platform that serves personalized ads to users on a social media app. The system should maximize long-term value by balancing user experience and advertiser outcomes under latency and scale constraints.
Task
Design an end-to-end ads serving system. Address:
- Data collection and event schema
- Feature engineering and feature store (offline/online parity)
- Model architecture: retrieval → ranking → re-ranking
- Real-time serving and latency budgets
- Feedback loops and training pipelines
- Exploration vs. exploitation strategies
- A/B testing design and evaluation
- Offline and online metrics to track
- Cold-start handling for users and ads
Assume standard ad objectives (e.g., CPC/CPA) and typical mobile feed constraints.
Hints
- Think multi-stage candidate generation (retrieval → ranking → re-ranking)
- Latency budgets and fallbacks per stage
- Point-in-time correct joins in the feature store
- Bandits for exploration/exploitation
- Calibration and counterfactual evaluation for offline metrics
Solution
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