Responsibilities
Team Introduction: TikTok is an international short video platform covering 150 countries and regions. We hope to discover real and interesting moments through TikTok and make life better. TikTok has offices around the world, with global headquarters in Los Angeles and Singapore, and offices in New York, London, Dublin, Paris, Berlin, Dubai, Jakarta, Seoul and Tokyo. The TikTok User Growth Team explores the growth potential of products and drives user growth with technology. The team not only practices user growth methodology in products and continuously consolidates growth capabilities, but also explores TikTok's multimodal product ecology to leverage the rapid growth of user scale. We will be extremely close to the business, and work with product and operation students to explore user growth methodology and continuously consolidate the growth capabilities of the business. Together, we will conduct insight analysis through data performance in the business to find business opportunities, analyze different business needs around different growth business models, and conduct technical design and implementation. We will also optimize and upgrade our architecture and system from a technical perspective, make pure technical level transformations, and use technology to drive business development and change. 1. Participate in the design and development of TikTok's paid advertising delivery system for multiple business directions 2. Participate in the development of scenario-based algorithm strategies such as advertising creation, budget & bid control, material quantity estimation, and effect attribution 3. Apply large model capabilities to explore material insights, e-commerce product testing, smart templates, AIGC material effect verification, and other work.
Qualifications
1. Computer science, software engineering, information technology or related majors 2. Knowledge of algorithms and data structures, operating systems, network programming, etc., with good programming and system design capabilities 3. Familiar with commonly used deep learning frameworks, and have a certain understanding of deep learning algorithms (LR, DNN, GBDT, etc.) 4. Project experience in recall, sorting, recommendation and other related strategies, and those who have participated in engineering and algorithm architecture design are preferred.
