About the Team- Shaping the Future of Global E-Commerce Experience
We are the Governance & Experience Algorithm Team — a trailblazing AI force at the forefront of building a trusted, efficient e-commerce ecosystem.
Established in 2020, we leverage Graph Neural Networks, Multi-Objective Optimization, Time Series Prediction, and Large Language Models (LLM) to tackle some of the most complex challenges in e-commerce: Quality Revolution
Combat low-quality products and fraudulent practices across millions of listings:
- Detect low-quality items, counterfeit goods, and false advertising.
- Identify "not-as-described" discrepancies between product claims and actual quality.
- Flag risky sellers through comprehensive quality assessments. Fulfillment & Logistics Experience Reinvention
Enhance delivery reliability and operational efficiency:
- Resolve delivery delays and non-receipt issues.
- Prevent inventory shortages via predictive analytics.
- Optimize warehouse operations for faster order fulfillment. Why Our Work Matters:
- We protect users globally, ensuring safe shopping experiences in international markets and beyond.
- Our innovations power TikTok’s mission to “Inspire Creativity, Bring Joy” by fostering trust and delight in every transaction.
- We’re not just solving problems—we’re redefining industry standards for e-commerce product, service, and logistics experiences. What You’ll Do
1. Lead Cutting - Edge AI Projects - Build graph-powered risk networks to uncover similar product clusters and high-risk seller/creator groups in emerging markets, reducing false advertising incidents in global regions.
- Develop LLM-based multi-modal systems using cross-modal fusion (text, images, behavioral data) to detect false advertising and low-quality products.
Deploy AI-powered product business suggestions to improve seller compliance and user trust.
- Lead time-series forecasting innovation for logistics performance metrics (e.g., delivery delays, cancellation rates), driving improvement in the e-commerce experience through predictive analytics.
2. Solve Complex Business Challenges - Develop multi-task models using MMoE and dynamic loss functions, driving measurable improvements in platform product, service, and logistics health.
- Transform supply chains by optimizing warehouse product quality qualification strategies to cut missing recalls and reduce warehouse costs.
- Unleash
RPD/RPR growth through causal inference frameworks, identifying hidden correlations between CCR and user behavior to design personalized recommendations.
3. Redefine Industry Standards - Optimize LLM reasoning with
DPO/GRPO to enhance fraud detection, outperforming traditional SFT methods.
Enhance tabular data modeling for better explainability in e-commerce risk assessments.
- Construct heterogeneous graphs modeling product - seller - creator - video - user relationships, enabling improvements in product lifecycle insights and detection of similar product patterns.
- Build unified time-series forecasting models across products, sellers, creators, videos, and users, achieving
SOTA performance in predicting inventory shortages and demand surges.