ML Engineer Intern: Multimodal Recommendation Foundations

TikTok; Seattle (WA); On-site; USD 65,000 - 92,000; Full time

Benefits offered by this job

Health insurance

Housing allowance

Paid holidays

Wellbeing benefits

Job summary

TikTok is seeking PhD interns to join the Recommendation Foundation team within Data – Global E-commerce in Seattle. The internship focuses on event-sequence driven generative recommendation, integrating large language and vision-language models, multimodal understanding, reinforcement learning, and system optimization.

Interns participate in the full training lifecycle, design multimodal semantic tokenizers, and develop LLM-native recommendation capabilities across retrieval, ranking, and

Qualifications

• Pursuing a PhD in Computer Science, Electrical Engineering, Mathematics, Statistics or related field.
• Strong foundation in machine learning and deep learning with 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

• Participate in the full training lifecycle of Recommendation Foundation Models, including pre-training, mid-training, and post-training.
• Design and train multimodal semantic tokenizers for recommendation items, leveraging multimodal foundation models to encode rich content into tokens.
• Develop LLM-native recommendation by incorporating tasks into large language model training and leveraging world knowledge.
• Build the next generation of recommendation systems powered by Foundation Models across retrieval, ranking, and end-to-end generation.

Skills

Python

LLMs

ML/DL

Research mindset

Education

PhD in CS/EE/Math/Stats

Tools

PyTorch

Job description

TikTok is seeking PhD interns to join the Recommendation Foundation team within Data – Global E-commerce in Seattle. The internship focuses on event-sequence driven generative recommendation, integrating large language and vision-language models, multimodal understanding, reinforcement learning, and system optimization.

Interns participate in the full training lifecycle, design multimodal semantic tokenizers, and develop LLM-native recommendation capabilities across retrieval, ranking, and