Design a Content Moderation Platform
Company: Bytedance
Role: Software Engineer
Category: ML System Design
Difficulty: medium
Interview Round: Onsite
Design a large-scale content moderation system for a social platform that accepts user-generated text, images, and videos. The system should detect policy violations such as spam, nudity, violence, or hate speech, and decide whether to allow content, block it, or send it to human review.
Discuss:
- Core APIs and services
- Storage for raw content, metadata, decisions, and audit logs
- Real-time and asynchronous moderation flows
- Model serving for multiple content modalities
- Feature pipelines and training data generation
- Human review queues and feedback loops
- Monitoring, reliability, and safe rollout of new models
The focus is not only on storage and APIs, but also on the ML infrastructure needed to operate the system in production.
Quick Answer: This question evaluates a candidate's ability to design scalable, ML-driven content moderation platforms, assessing competencies in system architecture, multi-modal model serving, feature and training data pipelines, storage and audit logging, human-in-the-loop workflows, and production ML infrastructure.