Recommendation Algorithm Engineer-TikTok Algorithm

nd pushing the boundaries of what's possible. Every line of your code will serve hundreds of millions of users. Our team is professional and goal-oriented, with an egalitarian and easy-going collaborative environment.

We welcome you to join us and make a global impact with TikTok!

Research Project Introduction:
As the world's leading short-video platform, TikTok faces multiple challenges in its recommendation systems, including data sparsity for new users leading to insufficient personalisation, high timeliness requirements for live steaming recommendations, difficulty in maintaining user interest diversity, and complex e-commerce recommendation system chains. Traditional recommendation methods heavily rely on historical behaviour modeling, which struggles with the cold-start problem for new users. Live-streaming recommendations demand real-time responsiveness to rapidly changing content dynamics (e.g., host interactions, traffic fluctuations) within extremely short time windows (typically within 30 minutes) posing higher demands on the system's real-time perception and decision-making capabilities.

Additionally, the immersive single-feed format amplifies the challenge of maintaining content diversity, requiring a careful balance between multi-interest learning and the risk of content drift caused by exploratory recommendations. The current e-commerce recommendation system follows a multi-stage funnel architecture (recall-ranking-re-ranking), which often leads to inconsistent chains, high maintenance costs, and an overreliance on short-term value prediction. This leads users to fall into content homogenization fatigue.

To address these pain points, this project proposes leveraging large language models (LLMs) and large model technologies to achieve significant breakthroughs. On one hand, LLMs-with their vast knowledge base and few-shot reasoning capabilities-can infer new users' potential intentions from registration data and external knowledge, thereby alleviating cold-start issues. On the other hand, by integrating graph neural networks (GNNs) and full-lifecycle user behavior sequences for modeling social preferences, we aim to improve the accuracy of interest prediction.

Additionally, the project explores the generalization capabilities, long-context awareness, and end-to-end modeling strengths of large models to simplify the e-commerce recommendation chains, enhance adaptability to real-time changes, and improve exploratory recommendation effectiveness. The ultimate goal is to build a more streamlined system with more accurate recommendations, enhancing user experience and retention while driving sustainable business growth.

Qualifications

1. Got doctor degree, preferably in Artificial Intelligence, Computer Science, Mathematics, or other related fields.
2. Strong programming skills with a good foundation in software design ability and coding practices.
3. Outstanding problem-solving and analytical skills, great passion for technology, and strong communication skills and teamwork.
4. Familiar with machine learning, natural language processing, and/or data mining. Prior experience in recommendation systems, computational advertising, or search engines is a plus.