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

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. Our internship program offers students hands-on experience, industry exposure, and opportunities to apply their knowledge to real-world challenges while building a strong foundation for personal and professional growth. Interns will gain practical experience, explore potential career paths, and participate in social events, learning programs, and development workshops alongside industry professionals. Candidates may apply to a maximum of two positions across Our Company and its affiliates globally. Applications will be considered in the order they are submitted. Applications are reviewed on a rolling basis, so we encourage you to apply early. Please clearly state your availability in your resume, including your start and end dates.

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.