GPU Application Platform Engineer Graduate (Server Research and Development) - 2027 Start (PhD)

About the team: Server Research and Development 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 architect, development and validation experience in the most advanced hardware infrastructure at a massive scale. We are looking for a self-motivated GPU/AI Application Platform Architect with focus on giant model system optimization.

We are looking for talented individuals to join our team. As a graduate, you will get opportunities to pursue bold ideas, tackle complex challenges, and unlock limitless growth. Successful candidates must be able to commit to an onboarding date by the end of the year. Please state your availability and graduation date clearly in your resume.

Responsibilities:

• Develop application benchmarks, tools and performance optimization methods for GPU/AI systems, including giant model training and inference systems such as LLM.
• 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 to improve training and inference system efficiency.
• Develop GPU/AI system TCO model, based on application benchmark and performance optimization.
• Work with industry consortiums and open standard committees to investigate the emerging standards or technologies, and to contribute our research results to the industry.
• Work with our technology partners and suppliers to setup POC or prototypes to evaluate and test the new technologies or architectural designs.Minimum Qualifications:
• Individuals who are completing or have recently completed a PhD degree in Electrical Engineering, Computer Engineering, Computer Science or related majors.
• Thesis topics in GPU/AI platform architecture and/or application performance optimization design or software hardware co-design.
• Deep understanding of computer system architecture, especially on GPU/AI SoC or Platform Architecture, Interconnect Fabric, and Memory sub-system.
• Experienced in GPU/AI system application performance optimization or software hardware co-design.
• Strong knowledge and proficiency in software development in C/C++, scripting languages such as Python.
• Understand the implementation of GPU/AI virtualization technology, deep learning architecture, and distributed systems.

Preferred Qualifications:

• Understand LLM model architecture, familiar with training and inference requirements on accelerator/memory/network.