About the team We are a group of applied machine learning engineers and data scientists that focus on general feed recommendations and E-commerce recommendations. We are developing innovative algorithms and techniques to improve user engagement and satisfaction, converting creative ideas into business-impacting solutions. We are interested and excited in applying large scale machine learning to solve various real-world problems. What you will do: • Participate in building large-scale (10 million to 100 million) recommendation algorithms and systems, including commodity recommendations, live stream recommendations, short video recommendations etc in TikTok. • Build long and short term user interest models, analyze and extract relevant information from large amounts of various data and design algorithms to explore users' latent interests efficiently. • Design, develop, evaluate and iterate on predictive models for candidate generation and ranking(eg. Click Through Rate and Conversion Rate prediction) , including, but not limited to building real-time data pipelines, feature engineering, model optimization and innovation. • Design and build supporting/debugging tools as needed. In order to enhance collaboration and cross-functional partnerships, among other things, at this time, our organization follows a hybrid work schedule that requires employees to work in the office 3 days a week, or as directed by their manager/department. We regularly review our hybrid work model, and the specific requirements may change at any time.
Minimum Qualifications - PhD or Master's degree in Computer Science, Statistics, Mathematics, or a related quantitative discipline. - 0-1 years of experience in machine learning, deep learning, data mining, or artificial intelligence. - Proficient in programming languages such as Python, C++, Java, or similar. Preferred Qualifications - Deep understanding of recommendation algorithms and personalization systems. - Excellent problem-solving and analytical skills. - Strong ability to communicate complex ideas effectively to both technical and non-technical audiences. - Experience with reinforcement learning techniques. - Proven modeling/algorithms competition records on Kaggle or top conferences’ challenges. - Proven programming competition records on ICPC, IOI or USACO. - Experience working with recommendation systems, computational advertising, search engine, E-commerce recommendation systems. - Publications in machine learning or related conferences or journals are highly desirable.
Read Full Description