Responsibilities
The Douyin recommendation team is responsible for Douyin's recommendation algorithm and is directly responsible for Douyin's core user experience, involving content consumption, social networking, live broadcast, push, local, and e-commerce scenarios. Our work includes the optimization of large-scale recommendation algorithms, the solution of complex constrained optimization problems, algorithm improvements in multiple academic fields such as CV/NLP, the design of recommendation architectures for multiple scenarios, and complex and in-depth analysis of product data. Here, you can delve into the improvement and optimization of machine learning algorithms and explore cutting-edge technologies you can apply algorithms to business through in-depth understanding and thinking of products you can also influence the future development direction of products through in-depth analysis of product and content ecology. 1. Responsible for the core business recommendation algorithm of Douyin, work closely with product, operation and other teams, deeply understand the development of Douyin recommendation business, and jointly formulate long-term and short-term business goals 2. Deeply participate in the research of core machine learning technology, form a complete and systematic methodology while solving specific problems, and continuously improve user experience 3. Research directions include but are not limited to: deep learning, graph neural networks, multi-task learning, learning to rank, model compression and acceleration, multimodal technology, etc., and be good at combining actual business problems to explore and research technology 4. Organize and promote intra-group and cross-departmental cooperation projects to help the team progress and newcomers grow.
Qualifications
1. Recommendation/search/advertising/machine learning related background, 3 years or more of work experience, with experience as a lead team or tech lead 2. Experience in large-scale recommendation algorithm and system development is preferred, with enthusiasm for recommendation algorithms, willingness to learn, think and innovate 3. Focus on cutting-edge technological progress, be passionate about solving challenging problems, and be able to make reasonable priority judgments 4. Experience in leading intra-group and cross-departmental cooperation projects, good business judgment and communication skills, good at communication and conflict resolution.