Machine Learning Engineer Graduate (E-Commerce User Growth) - 2027 Start (PhD)

Team Introduction: The TikTok e-Commerce Recommendation and Marketing Algorithm team is responsible for algorithm and big data work on e-Commerce innovation projects. Leveraging on our products, the team helps users discover and acquire great products, enriching their lives. In this team, we not only use recommendation and search algorithms to help users find items they are interested in but also employ risk control algorithms and intelligent platform governance algorithms to detect violations, ensuring a secure shopping experience. We build intelligent customer service technologies and large-scale product knowledge graphs to improve the efficiency of various transaction processes. Furthermore, we develop logistics and operations research algorithms to enhance supply chain efficiency, and we apply artificial intelligence to help merchants improve their operational capabilities. Our mission: To make high-quality products easily accessible, enabling everyone to enjoy a better life.

We are looking for talented individuals to join our team. As a graduate, you will get opportunities to pursue bold ideas, tackle complex challenges, and unlock limitless growth. Successful candidates must be able to commit to an onboarding date by the end of the year. Please state your availability and graduation date clearly in your resume.

Responsibilities:

• Participate in optimizing TikTok e-Commerce growth and marketing algorithms, including core capabilities such as user value modelling, personalized messaging, recommendations, and intelligent marketing.
• Establish a user lifecycle data and value system to address core pain points and business challenges related to TikTok Mall user growth.
• Contribute to the implementation of product and technical solutions for user growth engines, such as personalized push notifications/emails, recommendation handling, etc., to increase e-Commerce DAU and penetration rates.
• Optimize algorithms related to recall/sorting for new user recommendations, improving the relevance of traffic source handling and the accuracy and diversity of new user recommendations.
• Optimize intelligent marketing algorithms, utilizing uplift models and operations research methods to improve marketing efficiency and drive e-Commerce GMV growth. Minimum Qualification(s):
• Individuals who are completing or have recently completed a PhD degree in Computer Science, Electrical Engineering or related technical fields, with experience in C++ and Python; proficiency in at least one of the Big Data tools (e.g. Hive SQL/Spark/MapReduce) and at least one of the Deep Learning tools (e.g. TensorFlow/PyTorch).
• Strong foundation in algorithms and data structures, with excellent coding skills.
• Strong learning ability, curiosity, and good communication and teamwork skills.

Preferred Qualification(s):

• Candidates who have published papers in top AI conferences/journals or achieved notable results in ACM/Machine Learning competitions
• Experience in recommendation systems, advertising, user growth, intelligent marketing, or related fields, with expertise in LTV estimation, uplift modelling, operations research, sequence modelling, or multi-scenario modelling optimization is a plus.