Machine Learning Engineer Intern (E-Commerce Recommendation Foundation) - 2027 Start (PhD)

TikTok Inc.; San Jose (CA); On-site; USD 41,000 - 69,000; Full time

Job summary

TikTok Inc. is seeking PhD interns to contribute to the Recommendation Foundation team within Data - Global E-commerce. You will participate in end-to-end development of foundation models for recommendations, including multimodal content tokenization and LLM-integrated recommender experiments.

The role emphasizes research-minded software engineering, collaboration with experts, and rolling review of applications. Please specify your availability in your resume (start/end dates).

Qualifications

• Pursuing a PhD in CS, EE, math, statistics or related field.
• Strong foundation in ML and DL with interest in LLMs and generative recommendation.
• Proficiency in Python and DL 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 (pre-, mid-, post-training).
• Design and train multimodal semantic tokenizers for recommendation items using foundation models.
• Develop LLM-native recommendation by integrating tasks into large language model training.
• Build next-gen recommendation systems across retrieval, ranking, and end-to-end generative recommendation.

Skills

Machine learning

Deep learning

Research mindset

Self-driven

Education

PhD in Computer Science or related field

Tools

PyTorch

Python

Job description

Responsibilities

The Recommendation Foundation team within TikTok's Data - Global E-commerce organization is dedicated to building shared Recommendation Foundation Models across scenarios. We are exploring an event-sequence-driven generative recommendation paradigm that deeply integrates large language and vision-language models (LLMs/VLMs), multimodal understanding, reinforcement learning, and system optimization, advancing recommendation systems beyond click prediction toward general-purpose recommendation agents.

We believe the future of recommendation is not only about predicting clicks, but about understanding the relationships between people and content and generating new connections. We value original exploration and encourage research thinking and engineering practice equally. Every team member can propose hypotheses and validate ideas in an open environment; your code and publications may help shape the next generation of recommendation systems. We are looking for people with a general-intelligence mindset to redefine recommendation with us.

We are looking for talented individuals to join us for an internship. PhD internships at Our Company provide students with the opportunity to actively contribute to our products and research, as well as to the organization's future plans and emerging technologies.

Our dynamic internship experience blends hands-on learning, enriching community-building and professional development events, and collaboration with industry experts.

Applications will be reviewed on a rolling basis, so we encourage you to apply early. Please clearly state your availability in your resume (Start date, End date).

Responsibilities:

1. 1. Participate in the full training lifecycle of Recommendation Foundation Models, including pre-training, mid-training, and post-training.
2. 2. Design and train multimodal semantic tokenizers for recommendation items, leveraging multimodal foundation models to encode rich item content into discrete semantic tokens and raise the performance ceiling of Recommendation Foundation Models.
3. 3. Develop LLM-native recommendation by incorporating recommendation tasks directly into large language model training and leveraging world knowledge to improve recommendation quality.
4. 4. Build the next generation of recommendation systems powered by Recommendation Foundation Models, spanning retrieval, ranking, and end-to-end generative recommendation.

Qualifications

Minimum 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.

Preferred Qualifications

• - Experience with pre-training, mid-training, or post-training of LLMs or Foundation Models.
• - Research or project experience in generative recommendation, LLM-native recommendation, or multimodal semantic tokenization.
• - Publications on LLM-related topics at top-tier machine learning or natural language processing conferences, such as NeurIPS, ICML, ICLR, ACL, EMNLP, or NAACL, or strong achievements in major technical competitions.