Machine Learning Scientist Intern (Global E-Commerce Content Recommendation) - 2026 Summer (PhD)

Team Introduction Global E-Commerce Content Recommendation team plays a central role in the company, driving critical product decisions and platform growth. The team is made up of machine learning researchers and engineers, who support and innovate on production recommendation models and drive product impact. The team is fast-pacing, collaborative and impact-driven.

We are looking for talented individuals to join us for an internship in 2026. PhD Internships at our Company aim to provide students with the opportunity to actively contribute to our products and research, and to the organization's future plans and emerging technologies. PhD internships at Our Company provides students with the opportunity to actively contribute to our products and research, and to the organization's future plans and emerging technologies. Our dynamic internship experience blends hands-on learning, enriching community-building and development events, and collaboration with industry experts. Applications will be reviewed on a rolling basis - we encourage you to apply early. Please state your availability clearly in your resume (Start date, End date).

In this role, you'll have the opportunity to: - Build industry-leading recommendation system, improving user experience, content ecosystem and platform security; - Explore generative recommendation techniques, including Diffusion Models, prompt learning, and multimodal content generation, to unlock new capabilities in content discovery. - Build multi-model and cross-scenario systems enabling unified recommendation across livestreams, short videos, and search. - Deliver end-to-end machine learning solution to address critical product challenges; - Own the full stack machine learning system and optimize algorithms and infrastructure to improve recommendation performance. - Work with cross functional teams to design product strategies and build solutions to grow TikTok in important markets.

Minimum Qualifications: - Currently pursuing a PhD with a background in computer science, machine learning, or similar fields; - Good knowledge of theoretical and empirical research in addressing research problems; - Solid knowledge and experience with at least one popular deep learning framework (e.g., PyTorch,TensorFlow) and familiarity with deep neural network architectures.

Preferred Qualifications: - Research experience in one or more of the following fields: applied machine learning, machine learning infrastructure, large-scale recommendation system, market-facing machine learning product; - Strong first-author publications record in top AI conferences or journals(e.g., NeurIPS, ICML, ICLR, ACL, EMNLP, NAACL etc.); - Proficient in C/C++, Python, and shell programming languages, and have a deep understanding of data structure and algorithm design; - Internship experience in an AI research organization.

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