Machine Learning Engineer (Content Ecology & Creator) -E-commerce Governance

Global E-Commerce | Governance & Experience Algorithm Team About the TeamBuilding a Prosperous, Trusted, and Fair Global E-Commerce Ecosystem We are the Governance & Experience Algorithm Team, the AI guardians ensuring the long-term health of TikTok Shop’s global platform.

As our international business expands, our mission goes beyond traditional risk control. We are dedicated to constructing a prosperous, trusted content ecosystem and maintaining a fair, healthy environment for creators. We leverage LLM agents, RAG, GNN, and Sequence Modeling to solve complex governance challenges. We don't just block bad actors; we shape the rules of the game to ensure that creativity is rewarded, fairness is upheld, and the ecosystem thrives.

Our Core Mission:

• Trust & Quality: Ensuring users trust what they see, establishing a standard where "Good Content = Good Business."
• Creator Governance: Managing the full lifecycle of creators by identifying malicious intent (e.g., piracy, content mills) while protecting high-potential authentic creators.
• Ecosystem Fairness: using AI to ensure fair traffic distribution and prevent monopolies by bad actors, fostering a diverse and sustainable creator community.

What You’ll Do

1. Creator Governance & Quality Modeling - Signal-Driven Creator Profiling: aggregated underlying multi-modal signals (e.g., static frames, low-aesthetic detection, piracy fingerprints) to build comprehensive Creator Quality Scores. - Combat Low-Quality & Malicious Intent: Develop sequence-based models to detect and penalize creators engaging in "low-effort selling," "re-recording/piracy," and "matrix account spamming," effectively purging the ecosystem of noise. - LLM & RAG Intelligent Governance: Build LLM + RAG systems that dynamic interpret complex governance policies. Develop agents that not only flag risky creators but provide explainable reasoning to guide creator education and improvement. 2. Graph Intelligence & Syndicate Detection - Heterogeneous Graph Mining: Construct large-scale Heterogeneous Graphs (Creator-Product-Video-User) to uncover hidden relationships and organized bad actors (e.g., fake engagement rings, black-market account trading, sybil attacks). - Cross-Domain Risk Propagation: Utilize graph algorithms to track how risk propagates across different scenarios (Content vs. Shelf) and markets, predicting where bad actors will migrate next. 3. Ecosystem Strategy, Fairness & Optimization - Multi-Objective Optimization (MMoE/PLE): Develop advanced multi-task learning models to balance conflicting objectives—maximizing Ecosystem Prosperity and GMV while minimizing Governance Risk and User Complaints. - Fairness Algorithms: Design traffic regulation strategies that prevent the "rich get richer" effect for low-quality diverse content, ensuring fair exposure for high-quality, original creators.