Generative ML Engineer Intern - E-commerce Recommenders

TikTok; Seattle (WA); Presencial; USD 65.000 - 92.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Health insurance

Life insurance

Wellbeing benefits

Housing allowance

Descripción de la vacante

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

Formación

• 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).

Responsabilidades

• 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.

Conocimientos

Python

PyTorch

TensorFlow

JAX

Educación

PhD in Computer Science

Herramientas

GPUs

Descripción del empleo

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