Wasn't expecting a product-slash-system design hybrid for an ML role.
Start by clarifying the scope and objectives of the advertising system, such as ad formats, targeting, and business goals. Then, outline a high-level architecture covering data collection, feature engineering, model training, and serving, while discussing trade-offs and metrics. Finally, dive into ML-specific components like ranking models, user modeling, and real-time inference, ensuring alignment with Meta's scale and values.
Pro tip: Emphasize the importance of balancing user experience with advertiser value, and discuss how you would measure long-term impact through metrics like user retention and ad quality, not just short-term CTR.
Ask questions to understand the platform's scale, ad formats (e.g., pre-roll, mid-roll, display), targeting capabilities, and business objectives. Define key metrics like CTR, conversion rate, and user satisfaction.
Outline the main components: ad inventory management, targeting engine, ranking system, auction mechanism, and feedback loops. Describe data flow from user interactions to model updates.
Detail the ML pipeline: feature engineering (user, video, ad features), model choices (e.g., deep learning for CTR prediction, reinforcement learning for pacing), training infrastructure, and evaluation metrics.
Explain how models are deployed for low-latency predictions, including model serving, caching, and fallback strategies. Discuss scalability and reliability considerations.
Describe offline and online evaluation methods (A/B testing, counterfactual analysis), monitoring, and how to iterate on models to improve business and user metrics.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.