Machine Learning Engineer Graduate (Global E-Commerce, User Growth) - 2027 Start (PhD)

About the Team We are the User Growth Algorithm Team for Global E-Commerce — the core engine driving the explosive growth of TikTok's global e-commerce business. Here, we go beyond solving conventional recommendation and user reach problems; we are committed to reshaping the shopping experience of global consumers through cutting-edge promotion algorithms. As the Promotion Algorithm Team, we command massive budgets and enormous traffic flows. Through state-of-the-art algorithmic modeling and operations research optimization, we strive to achieve globally optimal ROI in budget allocation—directly driving scaled growth in overall MAC (Monthly Active Customers) and GMV. Here, every line of code you create will influence the purchasing decisions of tens of millions of users worldwide, and every model iteration will unlock tremendous business value. By joining us, you will have the opportunity to build on the unique ecosystem that fuses content with e-commerce to solve rare industry challenges in the joint optimization of "content recommendation + real-time subsidies."

We are looking for talented individuals to join our team. As a graduate, you will get opportunities to pursue bold ideas, tackle complex challenges, and unlock limitless growth. Successful candidates must be able to commit to an onboarding date by the end of the year. Please state your availability and graduation date clearly in your resume.

Responsibilities 1. Precision Subsidies & Intelligent Pricing: Model user demand elasticity across products and coupon values, combined with operations research optimization to solve for optimal budget allocation under strict constraints — maximizing GMV and conversion rate. 2. Intelligent Global Budget Optimization (Operations Research & Game Theory): Design globally optimal allocation and pricing algorithms based on elasticity modeling, enabling large-scale budgets to flow precisely across billions of users, massive product catalogs, and diverse scenarios for optimal budget efficiency. 3. Deep Synergy Between Traffic and Marketing (Vouchers × Traffic): Integrate real-time item-wise voucher capabilities into recommendation pipelines across short videos, live streaming, and marketplace. Break down barriers between marketing and recommendation to enable joint "traffic + subsidy" decision-making and global optimization. 4. Next-Gen Marketing Tech: Go beyond traditional discriminative uplift prediction. Research and productionize cutting-edge causal inference and AI technologies — including Uplift Modeling, E2E Modeling, RL, LLMs, and AI Agents — to directly address user conversion and repeat purchase behavior.