PhD ML Intern — Generative Recs for Live Commerce

TikTok; Seattle (WA); On-site; USD 69,000 - 90,000; Full time

Benefits offered by this job

Health insurance

Wellbeing benefits

10 paid holidays per year

Paid sick time

Housing allowance for non-remote

Job summary

TikTok is seeking a PhD-level intern to join the Global E-commerce Recommendation Live Algorithm team in Seattle. You will help build and optimize models spanning recall to ranking, with cross-domain signals across videos, live streams, and products.

You will explore generative and large-model techniques while collaborating with cross-functional teams to translate research into real impact. The role emphasizes hands-on learning, rigorous experimentation, and exposure to industry-scale systems.

Qualifications

• Currently pursuing a PhD in Computer Science, Engineering, Operations Research or a related technical discipline.

Responsibilities

• Build and optimize recommendation models across recall, pre-ranking, ranking, and mixed ranking.
• Develop cross-domain and multimodal modeling solutions that connect videos, live streams, products, and user behavior to power recommendations.
• Advance next-generation recommendation technologies, including generative recommendation, large models, reinforcement learning, and long-term value optimization.
• Partner with cross-functional teams to launch scalable solutions, run experiments, and turn research into measurable business impact.

Skills

Python

C++

Machine learning

PyTorch

Data structures

Algorithms

Problem solving

Self-driven

Education

PhD candidate in CS/Engineering

Job description

TikTok is seeking a PhD-level intern to join the Global E-commerce Recommendation Live Algorithm team in Seattle. You will help build and optimize models spanning recall to ranking, with cross-domain signals across videos, live streams, and products.

You will explore generative and large-model techniques while collaborating with cross-functional teams to translate research into real impact. The role emphasizes hands-on learning, rigorous experimentation, and exposure to industry-scale systems.