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Design a content moderation system

Last updated: Mar 29, 2026

Quick Overview

This question evaluates a candidate's competency in designing large-scale content moderation systems, covering distributed architecture, scalability and latency engineering, policy outcome modeling, abuse-resistance considerations, human-in-the-loop workflows, auditing and traceability, and operational trade-offs.

  • medium
  • TikTok
  • System Design
  • Software Engineer

Design a content moderation system

Company: TikTok

Role: Software Engineer

Category: System Design

Difficulty: medium

Interview Round: Technical Screen

Design a **content moderation system** for a large-scale product where users can submit content (e.g., text posts, images, videos, comments, or messages). Your design should cover: - **What is being moderated:** user-generated content before/after publishing. - **Policy outcomes:** allow, block, label, downrank, age-gate, or send to human review. - **Scale/latency goals:** handle high QPS; some content needs near-real-time decisions. - **Abuse resistance:** adversarial users trying to evade detection. - **Human-in-the-loop:** reviewer tooling, queues, SLAs, and escalation. - **Auditing/appeals:** traceability and user appeals. Provide an end-to-end architecture and justify key trade-offs (precision/recall, latency vs. quality, cost, and reliability).

Quick Answer: This question evaluates a candidate's competency in designing large-scale content moderation systems, covering distributed architecture, scalability and latency engineering, policy outcome modeling, abuse-resistance considerations, human-in-the-loop workflows, auditing and traceability, and operational trade-offs.

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TikTok logo
TikTok
Jan 22, 2026, 12:00 AM
Software Engineer
Technical Screen
System Design
2
0
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Design a content moderation system for a large-scale product where users can submit content (e.g., text posts, images, videos, comments, or messages).

Your design should cover:

  • What is being moderated: user-generated content before/after publishing.
  • Policy outcomes: allow, block, label, downrank, age-gate, or send to human review.
  • Scale/latency goals: handle high QPS; some content needs near-real-time decisions.
  • Abuse resistance: adversarial users trying to evade detection.
  • Human-in-the-loop: reviewer tooling, queues, SLAs, and escalation.
  • Auditing/appeals: traceability and user appeals.

Provide an end-to-end architecture and justify key trade-offs (precision/recall, latency vs. quality, cost, and reliability).

Solution

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