Research Intern (AML-Algorithm) - 2026 Start (PhD)

Responsibilities

You will be joining our Applied Machine Learning team, a central team responsible for delivering state-of-the-art solutions powering our company's recommendations, ads, and search systems across various products such as TikTok, Douyin. We own the end-to-end ML lifecycle, from ideation and research to building, deploying, and iterating on models in production. We are looking for candidates who are passionate about solving complex problems and have a strong foundation in machine learning theory and practice.

• Conduct research in machine learning for recommendation systems, with opportunities to explore large-scale recommendation models, generative recommendation, or reinforcement learning for personalization.
• Explore and prototype new modeling strategies that leverage multi-modal data (e.g., text, image, video) to enhance content and user understanding.
• Investigate long-term user behavior modeling and reinforcement learning techniques to improve sustained engagement.
• Collaborate with mentors and other researchers/engineers to test your ideas in real-world environments.
• Share findings through internal presentations, technical reports, and potentially external publications.

Qualifications

• Minimum Qualifications: Currently pursuing a Ph.D. degree in Computer Science, Computer Engineering, Electrical Engineering, or a related technical field. Strong research background in machine learning, deep learning, recommender systems, or related areas. Proficiency in at least one programming language such as Python or C++, and familiarity with deep learning frameworks (e.g., PyTorch, TensorFlow). Solid understanding of modern machine learning methods, such as transformers, large language models (LLMs), or multi-modal learning. Demonstrated ability to conduct independent research, with strong problem-solving and analytical skills.
• Preferred Qualification: Prior research or publications in top-tier ML/AI conferences (e.g., NeurIPS, ICML, ICLR, KDD, RecSys, WWW). Experience with large-scale ML systems or end-to-end ML pipelines is a plus. Passion for applying research to real-world challenges in recommendation, personalization, and user experience.