Recommendation Large Model Researcher-Global E-commerce

About TikTok
TikTok is the leading destination for short-form mobile video. At TikTok, our mission is to inspire creativity and bring joy. TikTok's global headquarters are in Los Angeles and Singapore, and we also have offices in New York City, London, Dublin, Paris, Berlin, Dubai, Jakarta, Seoul, and Tokyo.
Why Join Us
Inspiring creativity is at the core of TikTok's mission. Our innovative product is built to help people authentically express themselves, discover and connect - and our global, diverse teams make that possible. Together, we create value for our communities, inspire creativity and bring joy - a mission we work towards every day.
We strive to do great things with great people. We lead with curiosity, humility, and a desire to make impact in a rapidly growing tech company. Every challenge is an opportunity to learn and innovate as one team. We're resilient and embrace challenges as they come. By constantly iterating and fostering an "Always Day 1" mindset, we achieve meaningful breakthroughs for ourselves, our company, and our users. When we create and grow together, the possibilities are limitless. Join us.
Diversity & Inclusion
TikTok is committed to creating an inclusive space where employees are valued for their skills, experiences, and unique perspectives. Our platform connects people from across the globe and so does our workplace. At TikTok, our mission is to inspire creativity and bring joy. To achieve that goal, we are committed to celebrating our diverse voices and to creating an environment that reflects the many communities we reach. We are passionate about this and hope you are too.
Job highlights
Positive team atmosphere, Industry experts, Competitive compensation
Responsibilities
Team Introduction:
The team primarily focuses on recommendation services for the International E-commerce Mall, covering information flow recommendation in core scenarios such as the mall homepage, transaction funnels, product detail pages, stores & showcases. Committed to providing hundreds of millions of users daily with precise and personalized recommendations for products, live streams, and short videos, the team dedicates itself to solving challenging problems in modern recommendation systems. Through algorithmic innovations, we continuously enhance user experience and efficiency, creating greater user and social value.
Project Background/Objectives:
This project aims to explore new paradigms for large models in the recommendation field, breaking through the long-standing structures of recommendation models and Infra solutions, achieving significantly better performance than current baseline models, and applying them across multiple business scenarios such as Douyin short videos/LIVE/E-commerce/Toutiao. Developing large models for recommendation is particularly challenging due to the high demands on engineering efficiency and the personalized nature of user recommendation experiences. The project will conduct in-depth research across the following directions to explore and establish large model solutions for recommendation scenarios:
Project Challenges/Necessity:
The emergence of LLMs in the natural language field has outperformed SOTA models in numerous vertical tasks. In contrast, industrial-grade recommendation systems have seen limited major innovations in recent years. This project seeks to revolutionize the long-standing paradigms of recommendation model architectures and Infra in the recommendation field, delivering models with significantly improved performance and applying them to scenarios like Douyin short video and LIVE. Key challenges include:
High engineering efficiency requirements for recommendation systems;
Personalized nature of user recommendation experiences;
Effective content representation for media formats like short videos and live streams.
The project will address these through deep research in model parameter scaling, content/user representation learning, multimodal content understanding, ultra-long sequence modeling, and generative recommendation models, driving systematic upgrades to recommendation models.
Project Content:
1. Representation Learning Based on Content Understanding and User Behavior
2. Scaling of Recommendation Model Parameters and computing
3. Ultra-Long Sequence Modeling
4. Generative Recommendation Models
Involved Research Directions: Recommendation Algorithms, Large Recommendation Models.
Qualifications
1. Got doctor degree, with priority given to candidates in computer science, mathematics, or related fields.
2. Possess a solid foundation in machine learning and coding skills, with in-depth research experience in machine learning, NLP, CV, etc., and be proficient in major algorithms and data structures.
3. Candidates who have participated in or led key projects in search, advertising, recommendation, or large model domains are preferred.
4. Preference for those who have published papers at top international conferences, including but not limited to KDD, SIGIR, RecSys, ACL, NeurIPS, etc.
5. Demonstrate strong problem analysis and solving abilities, passion for technology, and be eager to drive and tackle various challenges.

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