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Bytedance·Software Engineer·Onsite - System Design / Architecture·Senior

SeniorPrefer not to say
May 2026

Summary

Bytedance system design round, heavy ML-infra flavor. The question was basically 'build TikTok's content moderation at scale' and they clearly wanted more than just a Kafka pipeline sketch.

Questions Asked (1)

Q1

Design a content moderation system for a high-traffic platform that handles text, images, and video at massive scale, combining automated ML-based decisions with human review queues, feedback loops back to training, and per-region regulatory compliance.

System DesignTechnical Trade-offsA/B Testing & Experimentation
Author's notes

This one sprawled fast.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and scale, then design a multi-stage pipeline: ingestion, automated classification, human review, enforcement, and feedback. Emphasize trade-offs between latency, accuracy, cost, and regulatory constraints, and describe how human feedback improves ML models.

Pro tip: Propose a tiered review system where high-confidence automated decisions bypass human review, and low-confidence or high-severity cases are routed to specialized human queues, optimizing cost and speed while maintaining safety.

1. Clarify Requirements and Scale

Ask about traffic volume, content types, latency SLAs, accuracy targets, regional regulations, and available resources. Define success metrics like precision/recall, review latency, and cost per decision.

2. Design Automated Moderation Pipeline

Outline ML models for text, image, and video (e.g., NLP, CNNs, video understanding). Include preprocessing, feature extraction, model inference, and confidence scoring. Discuss model serving at scale with batching and caching.

3. Integrate Human Review and Feedback Loop

Design queues for human moderators, prioritizing by confidence and severity. Implement feedback mechanisms to label data and retrain models, with active learning to improve efficiency.

4. Handle Regional Compliance and Enforcement

Describe how to apply region-specific policies (e.g., GDPR, local laws) via configurable rule engines. Ensure data residency and audit trails. Define enforcement actions (removal, warning, ban).

5. Address Trade-offs and Experimentation

Discuss trade-offs: latency vs. accuracy, automation vs. human cost, false positives vs. false negatives. Propose A/B testing for model updates and policy changes, with guardrail metrics.

Key Points to Mention

  • Scalable architecture: distributed processing, message queues, and microservices for ingestion and inference.
  • Multi-modal ML models: separate or joint models for text, image, and video, with confidence thresholds.
  • Human-in-the-loop: tiered review queues, moderator tooling, and quality assurance.
  • Feedback loop: data labeling, model retraining, and active learning to reduce human load.
  • Regulatory compliance: region-specific policies, data localization, and audit logging.
  • A/B testing and metrics: online experiments for model changes, measuring precision, recall, and user impact.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.