Design a Trustworthy Ranking System
Company: Snapchat
Role: Machine Learning Engineer
Category: ML System Design
Difficulty: medium
Interview Round: Onsite
Design a trustworthy ranking system for a large consumer platform that ranks items such as products, videos, or posts for each user. The system should optimize engagement and business outcomes while remaining safe, fair, and resistant to low-quality or manipulative content.
Discuss:
1. Product goals and trustworthiness constraints.
2. Candidate generation, feature pipeline, and ranking model.
3. Training labels and online serving.
4. How to handle delayed labels, such as purchases, returns, or long-term satisfaction signals that arrive hours or days later.
5. Offline evaluation, online experiments, and monitoring.
Quick Answer: This question evaluates machine learning system design skills for building trustworthy ranking systems, addressing candidate generation, feature pipelines, ranking models, training labels, delayed or long-term feedback, online serving, offline evaluation, experimentation, and monitoring within the ML System Design domain.