Design a game genre classifier
Company: Roblox
Role: Machine Learning Engineer
Category: ML System Design
Difficulty: medium
Interview Round: Onsite
Design an end-to-end machine learning system that classifies a video game into one or more genres from scratch. Assume you are building this for a gaming platform and must support newly launched games as well as established games. Discuss:
- Problem formulation: single-label vs. multi-label classification
- Data sources: game metadata, text descriptions, tags, screenshots, trailers, user behavior, and developer-provided information
- Label creation and data quality
- Feature engineering and model choices for text, image, and tabular signals
- Training pipeline, offline evaluation, and handling class imbalance
- Online serving architecture, latency constraints, and fallback behavior for cold-start games
- Monitoring, feedback loops, retraining strategy, and how to improve the system over time
Explain your design choices and trade-offs clearly.
Quick Answer: This question evaluates system design and machine learning competencies, including multi-modal modeling (text, images, tabular), data and label quality, model selection, training pipelines, serving architecture, latency and cold-start handling, and monitoring/MLOps for an end-to-end genre classification system.