Our E-commerce Recommendation Team is responsible for building up and scaling our recommendation system to provide the best shopping experience for our TikTok users.
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:
- Generative Recommendation Research: Drive the evolution of recommender systems from discriminative to generative paradigms;
explore frontier directions such as generative retrieval and generative re-ranking/blending;
continuously improve personalization capabilities while deeply optimizing the training and inference efficiency of generative models on GPUs.
- LLM for Recommendation Research: Leverage Large Language Models (LLMs), Reinforcement Learning (RL), and related techniques to enhance the semantic understanding and reasoning capabilities of recommender systems, addressing core business challenges in e-commerce scenarios (e.g., cold start, long-tail item distribution, and user intent understanding).
- Agentic Recommendation Research: Explore the construction of self-evolving agents and leverage agents to continuously optimize recommender systems;
drive the evolution of recommender systems toward agentic architectures capable of keenly perceiving user context and making real-time, personalized decisions and adjustments.
- Long-Term Value and User Experience Modeling: Explore replacing traditional heuristic rule-based systems with LLM and agent capabilities;
build next-generation algorithms for measuring and optimizing long-term value (LTV) and user experience, enabling sustainable growth of the platform ecosystem.