PhD Intern: Generative Recommendation & LLMs

TikTok; San Jose, Northern (CA, KY); Hybrid; USD 68,000 - 97,000; Part time

Benefits offered by this job

Health insurance

Paid holidays

Paid sick time

Housing allowance

Job summary

TikTok is seeking PhD candidates for an internship in the Recommendation Foundation team within the Data – Global E-commerce organization. You will work on event-sequence-driven generative recommendations, integrating LLMs and multimodal models while exploring RL and system optimization.

Rolling applications with start/end date details should be included in resumes. Interns gain hands-on learning, collaboration with experts, and opportunities to impact next-gen recommendation systems.

Qualifications

• Currently pursuing a PhD in Computer Science, Electrical Engineering, Mathematics, Statistics or a related discipline.
• Solid foundation in machine learning and deep learning, with strong interest in LLMs and generative recommendation.
• Proficiency in Python and experience with deep learning frameworks such as PyTorch.
• Self-driven, with a strong research mindset and solid engineering skills.

Responsibilities

• Contribute to the Recommendation Foundation team building models and exploring generative approaches.
• Collaborate across research and engineering to publish findings and validate ideas.
• Apply ML expertise to product-oriented research and prototype integrations.

Skills

Python

Machine learning

Deep learning

PyTorch

Research mindset

Engineering skills

Education

PhD in Computer Science, Electrical Engineering, Mathematics, Statistics or related discipline

Tools

PyTorch

Job description

TikTok is seeking PhD candidates for an internship in the Recommendation Foundation team within the Data – Global E-commerce organization. You will work on event-sequence-driven generative recommendations, integrating LLMs and multimodal models while exploring RL and system optimization.

Rolling applications with start/end date details should be included in resumes. Interns gain hands-on learning, collaboration with experts, and opportunities to impact next-gen recommendation systems.