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

Description

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.

Requirements

Minimum Qualifications:
- Bachelor's degree or above in computer science or related field
- Proficient in Python/C++ with strong hands-on experience in PyTorch or TensorFlow
- Deep expertise in at least one of the following areas: NLP/LLM (Agents/Tuning), Graph Neural Networks (GNN), Sequence Modeling, or Machine Learning
- 1+ years of experience in Content Governance, Trust & Safety, Creator Ecology, or Advertising/Search/Recommendation

Preferred Qualifications
- Cutting-Edge Application: Experience with RAG, DPO/RLHF, or Multi-Modal Representation Learning in a production environment is highly preferred
- You view problems through an ecosystem lens—caring about Health, Fairness, and Diversity, not just binary classification metrics (Precision/Recall)
- Ability to translate abstract business goals (e.g., "Improve Creator Fairness") into concrete mathematical definitions and model targets
- Strong communication skills to articulate algorithmic strategies to Policy, Operations, and Product teams
- You enjoy the "cat and mouse" game of outsmarting evolving bad actor techniques