Research Intern; AML-Algorithm PhD

Position: Research Intern (AML-Algorithm) - 2026 Start (PhD)
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 Tik Tok, 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.

We are looking for talented individuals to join us for an internship in 2026. PhD Internships at Byte Dance 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 Byte Dance 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:

- 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.

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, Tensor Flow).

- 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, Rec Sys, 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. By submitting an application for this role, you accept and agree to our global applicant privacy policy, which may be accessed here: