Start by clarifying requirements and constraints, then outline the end-to-end pipeline from data collection to real-time serving. Focus on the model architecture, feature engineering, and scalability aspects, emphasizing trade-offs and Reddit-specific considerations.
Pro tip: Highlight the importance of negative sampling and handling class imbalance, as comment prediction is inherently sparse. Also, discuss how to incorporate real-time user and post features without introducing latency.
Ask about scale (users, posts, QPS), latency requirements, data availability, and business goals. Confirm that the output is a probability score per post for a given user.
Describe data sources (user interactions, post content, metadata) and feature types (user demographics, historical behavior, post embeddings, cross features). Discuss offline/online feature consistency and feature stores.
Choose a model architecture (e.g., two-tower, gradient boosted trees, or deep neural networks) suitable for large-scale ranking. Explain training pipeline, loss function (e.g., binary cross-entropy), negative sampling, and evaluation metrics (AUC, log loss).
Design a scalable serving system with low latency: precompute user and post embeddings, use approximate nearest neighbor search for candidate generation, and a lightweight ranker for scoring. Discuss caching, batching, and fallback strategies.
Outline monitoring for model performance, data drift, and system health. Describe A/B testing framework and retraining cadence to keep the model up-to-date.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.