Design trending livestream discovery
Company: Whatnot
Role: Machine Learning Engineer
Category: ML System Design
Difficulty: medium
Interview Round: Technical Screen
Design a system for a live-commerce platform that surfaces **trending livestreams** to users.
Assume an ML model for scoring trendiness or relevance already exists and can be called as a service. Your task is to design the end-to-end ML system around that model.
Discuss:
- the product goal and success metrics
- what events and features you would collect
- how to generate candidate livestreams
- how to combine business rules, real-time signals, and the model score
- the online and offline architecture
- latency, freshness, and scaling requirements
- how to prevent stale, low-quality, spammy, or ended streams from being recommended
- experimentation, monitoring, and failure handling
Follow-up: **How would you ensure that a stream shown in the trending feed is actually still live?**
Quick Answer: This question evaluates expertise in designing end-to-end ML-driven recommendation systems, including competencies in real-time feature engineering, candidate generation, model serving, business-rule integration, monitoring, experimentation, and operational trade-offs for live content.