Research Scientist Intern (TikTok - NextGen Recommendation) - 2026 Start (PhD)

g technologies.

PhD internships at TikTok provide 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).

Responsibilities:

• Design and develop next-generation large-scale recommendation systems optimized for personalized, engaging, and scalable user experiences.
• Leverage state-of-the-art machine learning and deep learning techniques, including large model technologies (LLM and MLLM, etc), to enhance recommendation performance and accuracy.
• Collaborate with cross-disciplinary teams, including infrastructure engineers, pmo, and researchers, to create advanced systems that improve recommendation relevance, diversity, and user engagement.

Qualifications

Minimum Qualifications:

• Currently pursuing PhD Degree in Computer Science, Machine Learning, Artificial Intelligence, Statistics, or a related field.
• Hands-on experience in one or more of the following areas: Large Language Models (LLM), Machine Learning, Deep Learning, Recommender Systems, Data Mining, or Natural Language Processing
• Familiarity with PyTorch or TensorFlow, solid foundation in data structures and algorithms.
• Excellent communication and teamwork skills, and a passion for learning new techniques and tackling challenging problems

Preferred Qualifications:

• Strong engineering and infrastructure development skills, with hands-on experience in building and optimizing distributed systems and processing large-scale online/offline dataflow. Proficiency in CUDA programming (experience with Triton) is highly desirable.
• Prior research/industry experience in at least two of the following areas-multimodal content understanding, personalized recommendation, or large-scale cross-domain optimization-is a significant advantage.
• In-depth knowledge and expertise in large-scale Transformer architectures, including mastery of the latest optimization techniques such as Sparse Attention, Linear Attention, Flash Attention, and other cutting-edge methods to enhance model performance and efficiency.
• Publications at major AI-related conferences such as NeurIPS, ICML, ICLR, AAAI, IJCAI, ACL, NAACL, EMNLP, CVPR, ICCV, ECCV, KDD, ICDM, SDM, RecSys, or simply on arXiv but with large impact
• Strong track record in AI-related competitions, or participation in public/open-source AI-related projects of high visibility.