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

About the Team The E-commerce Algorithm Team powers TikTok Shop's intelligent commerce ecosystem, connecting users with quality products and sellers through personalized recommendations, search, live streaming, and short-form content. Our team of Machine Learning Engineers, Applied Scientists, and Data Scientists develops large-scale AI and machine learning solutions that enhance user experience, drive merchant growth, and create business impact. We work on cutting-edge technologies across recommendation systems, search, ranking, LLM applications, and marketplace optimization, transforming innovative research into products used by millions worldwide.

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 1. Design and develop ML algorithms and intelligent systems for large-scale e-commerce scenarios: recommendation, ranking, retrieval, forecasting, content understanding, pricing, governance, and conversational AI. 2. Apply machine learning, deep learning, NLP, CV, multimodal models, and LLMs to solve real-world business problems. 3. Build end-to-end algorithm solutions: data processing, feature engineering, model training, evaluation, deployment, monitoring, and continuous optimization. 4. Develop scalable systems for real-time or offline learning and inference on large-scale structured and unstructured data. 5. Partner with product, engineering, data science, and business teams to translate needs into high-impact technical solutions. 6. Run experiments, analyze model performance, debug production issues, and iterate rapidly to improve quality and efficiency. 7. Explore next-gen intelligent systems: foundation models, generative recommendation, multimodal reasoning, and AI agents.