Technical VP - Computing Bus System Research

Job description

Huawei Canada has an immediate permanent opening for a Technical VP. About the team: The Computing Data Application Acceleration Lab aims to create a leading global data analytics platform organized into three specialized teams using innovative programming technologies. This team focuses on full-stack innovations, including software-hardware co-design and optimizing data efficiency at both the storage and runtime layers. This team also develops next-generation GPU architecture for gaming, cloud rendering, VR/AR, and Metaverse applications. One of the goals of this lab are to enhance algorithm performance and training efficiency across industries, fostering long-term competitiveness. About the job: Conduct in-depth insight and analysis of industry cutting-edge bus architectures and technologies, develop long-term technology plans and roadmaps, and lead technical decision-making. Build competitive bus architectures and interconnection protocols for AI/general computing scenarios, in align with mainstream architectures and protocols in the industry. Drive the continuous evolution of bus technology, ensuring an industry-leading competitive edge of large-scale computing cluster solutions. Deeply understand the chip architecture and software framework. Implement system-level innovative solutions centered around bus architecture and protocols. Drive co-innovation across chip design, hardware/software, and solutions to ensure business success. About the ideal candidate： Solid foundational knowledge in computer science and computer architecture. In-depth knowledge of bus/network protocols such as PCIe, CXL, and InfiniBand. Familiarity with technologies related to in-order delivery, congestion, retransmission, multipath, flow control, QoS, network measurement, fault tolerance, and reliability. Experience in architecture design and R&D is preferred in scenarios such as high bandwidth, low latency, and homogeneous/heterogeneous computing interconnection. Familiarity with AI large model training, big data, search and push, distributed database, and other application scenarios. Experience in large-scale computing cluster architecture design, hardware-software co-optimization for performance, and relevant implementation outcomes are preferred. Experience in leading the planning, architecture design, and implementation of relevant chips or products. Strategic vision and technical leadership, with strong communication skills and good insight.