Machine Learning Algorithm Research Engineer

Team Introduction:
TikTok Research & Development (R&D) Team:
The TikTok R&D team is dedicated to building and maintaining industry-leading products that drive the success of TikTok’s global business. By joining us, you'll work on core scenarios such as user growth, social features, live streaming, e-commerce consumer side, content creation, and content consumption, helping our products scale rapidly across global markets. You'll also face deep technical challenges in areas like service architecture and infrastructure engineering, ensuring our systems operate with high quality, efficiency, and security. Meanwhile, our team also provides comprehensive technical solutions across diverse business needs, continuously optimizing product metrics and improving user experience.

Here, you'll collaborate with leading experts in exploring cutting-edge technologies and 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.

Why Join Us?
1. Co-create with the team: Creativity is at the core of TikTok. Whether it's building the product or shaping the team, we strive to spark imagination and deliver impact — for ourselves, the platform, the communities we serve, and society as a whole.
2. Grow through challenges: At TikTok, you’ll tackle highly challenging projects that drive industry breakthroughs and global influences. With hundreds of millions of users, there’s always an opportunity to introduce new technologies and ideas that shape better user experiences. Every challenge is a chance to learn, innovate, and grow.
3. Work style and culture: We value practical problem-solving and the pursuit of excellence in everything, encouraging everyone to work with the mindset of “Always Day 1.” Our company culture is diverse and inclusive, where everyone collaborates as equal and operates in an agile and flexible environment that empowers creativity.
4. Recognition and rewards for excellence: We grow together with exceptional people — and it’s never too late to join. We’ve enhanced our reward system to recognize high performance, offering more opportunities for outstanding individuals to take on key projects and fully unleash their potential.

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.