GPU/AI Application Platform Architect - San Jose

岗位职责 / Responsibilities

Server platform team is responsible for architecting, designing and building 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 experiences in most advanced hardware infrastructure at massive scale.

We are looking for a self-motivated GPU/AI Application Platform Architect with the following responsibilities:

- Track GPU/AI LLM technology from industry and partner vendors. Evaluate and test the new part or technology, integrate the technology into the system.

- Drive GPU/AI LLM platform customization via application performance optimizations and architecture explorations to increase system Perf/TCO and/or reduce system TCO.

- Drive GPU/AI LLM new technology solution study and implementation.

- Evaluate GPU system performance under state-of-art LLM applications.

- Work with industry consortiums and open standard committees to investigate the emerging technologies or standards, and contribute our research results and visions 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.

- International travel requirement: up to four times per year, including but not limited to China, Europe, and South Asia. Candidates must have a valid passport and be able to obtain the necessary visas.

任职要求 / Requirements

Minimum Qualifications

- Master’s degree or higher in Electrical Engineering, Computer Engineering, Computer Science or related majors.

- 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.

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

- Understand the implementation of GPU/AI virtualization technology, deep learning architecture, and distributed system.

Preferred Qualifications

- 3 years experience in GPU/AI LLM platform architecture and/or application performance optimization design or software hardware co-design.

- Demonstrated experience in working collaboratively with cross-functional teams.