Software Engineer Intern (TikTok Global E-Commerce Recommendation & Search Architecture) - 2027 Start

E-commerce is a new and fast growing business that aims at connecting all customers to excellent sellers and quality products, through E-commerce live-streaming, E-commerce short videos, and commodity recommendation. Our E-commerce Recommendation and Search Infra team is responsible for building up and optimizing the infrastructure for such recommendation and search systems, so as to provide the best experience for our users. We work closely with applied machine learning engineers and build scalable systems to support all kinds of innovative algorithms and techniques. The Search team is mainly responsible for the search algorithm innovation and architecture research and development of TikTok Shop. We use cutting-edge machine learning technology for end-to-end modeling and continuous innovation and breakthroughs, while focusing on the construction and performance optimization of distributed systems and machine learning systems.

We are looking for talented individuals to join us for an internship. Our internship program offers students hands-on experience, industry exposure, and opportunities to apply their knowledge to real-world challenges while building a strong foundation for personal and professional growth. Interns will gain practical experience, explore potential career paths, and participate in social events, learning programs, and development workshops alongside industry professionals. Candidates may apply to a maximum of two positions across Our Company and its affiliates globally. Applications will be considered in the order they are submitted. Applications are reviewed on a rolling basis, so we encourage you to apply early. Please clearly state your availability in your resume, including your start and end dates. Successful candidates must be able to commit to at least 3 months long internship period.

Responsibilities - Design, build and optimize distributed training infrastructure and low-latency online inference systems for large-scale recommendation models and Large Language Models - Develop high-performance GPU kernel implementations and efficient inter-node communication primitives to improve training and inference efficiency - Build compiler optimization passes and operator fusion technologies for deep learning frameworks to accelerate model execution