Machine Learning Engineer Intern (E-Commerce Recommendation Live) - 2027 Start (PhD)

Location: San Jose, California, United States of America Department: R&D

The Global E-commerce Recommendation Live Algorithm team is responsible for the core recommendation stack for live commerce, covering the full pipeline from recall and pre-ranking to ranking and mixed ranking. The team operates in a highly dynamic environment where live room status changes in real time, conversion signals are sparse, and user intent must be understood across content, commerce, and transaction scenarios.

By combining generative recommendation, large recommendation models, multimodal representation learning, and cross-domain value modeling, the team works on some of the most important algorithmic problems in live commerce. Our goal is to improve user experience, optimize ecosystem efficiency, and drive sustainable business growth for TikTok Shop across global markets.

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

• Build and optimize recommendation models across recall, pre-ranking, ranking, and mixed ranking to improve GMV, conversion, watch time, and long-term user value.
• Develop cross-domain and multimodal modeling solutions that connect videos, live streams, products, and user behavior to better power live commerce recommendations.
• Advance next-generation recommendation technologies, including generative recommendation, large recommendation models, reinforcement learning, and long-term value optimization.
• Partner with cross-functional teams to launch scalable solutions, run experiments, and turn research into measurable business impact.

Minimum Qualifications

• Currently pursuing a PhD in Computer Science, Engineering, Operations Research or a related technical discipline.
• Solid foundation in machine learning and at least one of the following areas: recommendation systems, search, advertising, NLP, multimodal learning, or large-scale applied AI.
• Strong programming skills in Python or C++, and hands-on experience with deep learning frameworks such as PyTorch.
• Good understanding of data structures, algorithms, and large-scale model training or production machine learning systems.
• Strong analytical and problem-solving skills, with the ability to translate business problems into effective modeling solutions.
• Self-driven and results-oriented, with the ability to take ownership of model iteration and online impact from end to end.

Preferred Qualifications

• Experience in recommendation systems, especially in live commerce, e-commerce, search, ads, or other large-scale consumer products.
• Experience with generative recommendation, large recommendation models, retrieval and ranking systems, or related recommendation architecture upgrades.
• Experience with LLMs or multimodal foundation models, including pre-training, post-training, representation learning, contrastive learning, SFT, or RL-based optimization.
• Experience in cross-domain transfer learning, LTV modeling, long-term value optimization, causal inference, or debiasing.
• Experience with long-sequence user behavior modeling, multi-task learning, multi-interest modeling, or large-scale distributed training and inference optimization.
• Publications in top-tier conferences such as NeurIPS, ICML, ICLR, KDD, ACL, CVPR, SIGIR, or RecSys, or strong achievements in major technical competitions.
• Strong curiosity about new technologies, fast learning ability, and a passion for solving challenging real-world problems.