AI/LLM Network Research Intern (High Speed Network) - 2027 Start (PhD)

ByteDance Networking brings together innovative ideas and technologies from network architecture, software defined networking (SDN), network virtualization, switch software and hardware co-design, and high-speed networking, to create hyperscale data-center networking solutions that power several of the most popular apps of the world such as Douyin and TikTok which serve hundreds of millions of users around the globe.<br/><br/>ByteDance Networking is responsible for designing, building, and operating the global, intelligent network infrastructure to meet the requirements of high availability, scalability, and high-performance. By joining this team, you will gain marketable software development and/or network operation experiences in data center networking at massive scale.<br/><br/>We are looking for talented individuals to join us for an internship. PhD internships at Our Company provide students with the opportunity to actively contribute to our products and research, as well as to the organization's future plans and emerging technologies.<br/>Our dynamic internship experience blends hands-on learning, enriching community-building and professional development events, and collaboration with industry experts.<br/>Applications will be reviewed on a rolling basis, so we encourage you to apply early. Please clearly state your availability in your resume (Start date, End date).<br/><br/>Responsibilities:<br/>- Design, optimization, implementation and deployment of high-performance transport protocols to support AI/LLM applications.<br/>- Design, optimization, implementation and deployment of congestion control algorithms to support AI/LLM applications..<br/>- Research and development of high-performance AI communication framework, network protocol stacks, and co-design optimization of host-network-application to improve the scalability, reliability and performance of AI/LLM networks.<br/>- Follow the latest technologies from academia and industry, identify the innovative parts of the system and present in academic papers.<br/><br/> annually.<br/>