Generative ML Engineer Intern — E‑Commerce Recommenders

TikTok; San Jose (CA); Presencial; USD 51.000 - 73.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Health insurance

Life insurance

Wellbeing benefits

Housing allowance

Descripción de la vacante

TikTok Inc. is offering an internship in San Jose focused on advancing recommender systems through generative methods, LLMs, and agentic architectures. You will work on cutting-edge research spanning generative retrieval, re-ranking, and efficiency for GPU training/inference.

The role emphasizes hands-on ML experience with Python and DL frameworks, with exposure to live commerce-related recommendations, and opportunities to publish research or contribute to state-of-the-art models.

Formación

• Currently pursuing an Undergraduate degree in Software Development, Computer Science, Computer Engineering or a related discipline.
• Solid foundation in machine learning and deep learning, with research or project experience in at least one of the following areas: large language models, reinforcement learning, generative models, recommender systems or information retrieval.
• Proficient in Python and at least one mainstream deep learning framework (e.g., PyTorch, TensorFlow, JAX). Strong problem-solving skills and passion for tackling complex, open-ended research problems.

Responsabilidades

• Generative Recommendation Research: Drive the evolution of recommender systems from discriminative to generative paradigms; explore frontier directions such as generative retrieval and generative re-ranking/blending; continuously improve personalization capabilities while deeply optimizing the training and inference efficiency of generative models on GPUs.
• LLM for Recommendation Research: Leverage Large Language Models (LLMs), Reinforcement Learning (RL), and related techniques to enhance the semantic understanding and reasoning capabilities of recommender systems, addressing core business challenges in e-commerce scenarios (e.g., cold start, long-tail item distribution, and user intent understanding)
• Agentic Recommendation Research: Explore the construction of self-evolving agents and leverage agents to continuously optimize recommender systems; drive the evolution of recommender systems toward agentic architectures capable of keenly perceiving user context and making real-time, personalized decisions and adjustments.
• Long-Term Value and User Experience Modeling: Explore replacing traditional heuristic rule-based systems with LLM and agent capabilities; build next-generation algorithms for measuring and optimizing long-term value (LTV) and user experience, enabling sustainable growth of the platform ecosystem.

Conocimientos

Python

Machine learning

Deep learning

Recommender systems

Educación

Undergraduate degree in Software Development/CS/CE

Descripción del empleo

TikTok Inc. is offering an internship in San Jose focused on advancing recommender systems through generative methods, LLMs, and agentic architectures. You will work on cutting-edge research spanning generative retrieval, re-ranking, and efficiency for GPU training/inference.

The role emphasizes hands-on ML experience with Python and DL frameworks, with exposure to live commerce-related recommendations, and opportunities to publish research or contribute to state-of-the-art models.