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

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 us for an internship. PhD internships at Our Company provide students with the opportunity to actively contribute to our products and research, as well as to the organization's future plans and emerging technologies.

Our dynamic internship experience blends hands-on learning, enriching community-building and professional development events, and collaboration with industry experts.

Applications will be reviewed on a rolling basis, so we encourage you to apply early. Please clearly state your availability in your resume (Start date, End date).

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 Qualifications

• Currently pursuing a PhD in Computer Science, Electrical Engineering, Mathematics, Statistics or a related discipline.
• Solid ML and engineering fundamentals: you understand the math behind the models, and you write clean, efficient, reproducible code with a strong command of algorithms and data structures.
• Deep research or engineering practice in at least one of: LLMs / foundation models, NLP, CV, RL, or recommendation / search / ads — and you can articulate why you made the choices you made, and where they fell short.
• Genuine enthusiasm for LLM / LRM techniques: you want frontier methods live in production, not parked at offline metrics.
• Strong problem definition and decomposition: faced with an ambiguous problem that has no standard answer, you find your own foothold.

Preferred Qualifications

• 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.