Generative ML Engineer Intern - E-commerce Recommenders

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

Benefits offered by this job

Health insurance

Life insurance

Wellbeing benefits

Housing allowance

Job summary

TikTok is offering a PhD internship on our E-commerce Recommendation Team in Seattle. You will contribute to cutting-edge research on generative recommender systems, LLM-driven improvements, and agentic architectures to optimize user experience and long-term value.

Ideal candidates are pursuing a PhD with deep learning experience in LLMs, RL, and recommender systems, proficient in Python and major DL frameworks, and eager to tackle real-world, scalable research challenges in a fast-moving tech

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 research or project experience in LLMs, RL, generative models, recommender systems or information retrieval.
• Proficient in Python and at least one mainstream deep learning framework (e.g., PyTorch, TensorFlow, JAX).

Responsibilities

• Generative Recommendation Research: evolve recommender systems toward generative Paradigms; explore generative retrieval and re-ranking; optimize training and inference on GPUs.
• LLM for Recommendation Research: leverage LLMs, RL, and related techniques to enhance semantic understanding in recommender systems for e-commerce challenges.
• Agentic Recommendation Research: build self-evolving agents to continuously optimize recommender systems and enable real-time, personalized decisions.
• Long-Term Value and User Experience Modeling: develop algorithms to measure and optimize LTV and user experience for sustainable growth.

Skills

Python

PyTorch

TensorFlow

JAX

Education

PhD in Computer Science

Tools

GPUs

Job description

TikTok is offering a PhD internship on our E-commerce Recommendation Team in Seattle. You will contribute to cutting-edge research on generative recommender systems, LLM-driven improvements, and agentic architectures to optimize user experience and long-term value.

Ideal candidates are pursuing a PhD with deep learning experience in LLMs, RL, and recommender systems, proficient in Python and major DL frameworks, and eager to tackle real-world, scalable research challenges in a fast-moving tech