ML Engineer Intern – Live Commerce & Generative Recs

TikTok; San Jose (CA); On-site; USD 68,000 - 97,000; Part time

Benefits offered by this job

Health insurance

Paid holidays

Housing allowance

Job summary

TikTok is seeking PhD-level researchers for an internship in the Global E-commerce Recommendation Live Algorithm team in San Jose. The role focuses on building and optimizing large-scale recommendation models across recall to mixed ranking, combining multimodal data, and advancing generative and RL-based methods to improve GMV and user engagement.

You will collaborate with cross-functional teams, translate research into scalable solutions, and state availability clearly in your resume.

Qualifications

• PhD in CS, Engineering, OR related technical discipline.
• Strong ML foundation with recommendation systems, search, ads, NLP or multimodal learning.
• Proficient in Python or C++ and PyTorch.
• Solid understanding of data structures, algorithms and large-scale training/production systems.
• Strong analytical and problem-solving skills and business sense.
• Self-driven with ownership of model iteration and online impact.

Responsibilities

• Build and optimize recommendation models across recall, pre-ranking, ranking and mixed ranking to improve GMV, conversion, watch time and long-term user value.
• Develop cross-domain and multimodal modeling solutions connecting videos, live streams, products and user behavior.
• Advance next-generation recommendation tech including generative models, RL and long-term value optimization.
• Partner with cross-functional teams to launch scalable solutions, run experiments and translate research into measurable business impact.

Skills

Python

C++

PyTorch

Machine learning

Data structures

Problem solving

Ownership

Education

PhD in Computer Science or related

Tools

Git

Job description

TikTok is seeking PhD-level researchers for an internship in the Global E-commerce Recommendation Live Algorithm team in San Jose. The role focuses on building and optimizing large-scale recommendation models across recall to mixed ranking, combining multimodal data, and advancing generative and RL-based methods to improve GMV and user engagement.

You will collaborate with cross-functional teams, translate research into scalable solutions, and state availability clearly in your resume.