GPU/AI Application Platform Engineer Intern (Server Platform) - 2027 Start (PhD)

About the Team:<br/>ByteDance's Server platform team is responsible for architecting, designing, and building the best server and storage system to meet the requirements of high-performance, low cost and easy to operate. By joining this team, you will work with the best engineers and talents in this industry and have a broad opportunity to get in touch with the latest AI application system and newly emerged technology in computing, storage and silicon validation. You will gain remarkable hardware architecture, development and validation experience in the most advanced hardware infrastructure at a 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/>- Develop application benchmarks, tools and performance optimization method for GPU/AI system.<br/>- Identify the system bottleneck/opportunity with deep system-level data-driven study, explore innovative options through SW-HW co-design, and lead them towards implementation.<br/>- Develop GPU/AI system TCO model, based on application benchmark and performance optimization.<br/>- Work with industry consortiums and open standard committees to investigate the emerging standards or technologies, and contribute our research results to the industry.<br/>- Work with our technology partners and suppliers to setup POC or prototypes to evaluate and test the new technologies or architectural designs.<br/><br/> annually.<br/>