Principal Scientist - Software / Hardware Co-design
Join to apply for the Principal Scientist - Software / Hardware Co-design role at Huawei Canada .
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. They also develop next-generation GPU architecture for gaming, cloud rendering, VR / AR, and Metaverse applications. One goal of this lab is to enhance algorithm performance and training efficiency across industries, fostering long-term competitiveness.
About the job :
- Build an accurate and universal AI performance model based on mainstream AI acceleration technologies to support theoretical analysis.
- Track emerging hardware designs in the industry, conduct in-depth insights and survey analysis, and identify the direction of key cutting-edge technologies.
- Collaborate with the AI research team to identify key performance bottlenecks in future AI workloads, and define key algo-hw co-design features of next-generation chips for low cost, high throughput, scalability, and stability.
- Perform performance modeling of representative AI workloads with state-of-the-art training & inference algorithms on different hardware specifications for quantitative analysis of compute, memory, IO, and interconnect.
- Lead the team for breakthroughs in acceleration algorithms balancing model quality and compute efficiency.
- Track emerging algorithm-hardware co-design technologies, conduct in-depth analysis, and understand main directions and trends of cutting-edge technologies.
Job requirements :
Master's or Doctoral degree in Computer Science or Electronic Engineering.At least 5+ years of experience in low-level computing algorithm development, AI accelerators, large-scale parallel computing, or high-performance system design.Deep understanding of large language models / multimodal models, AI software stacks (operators, compilers, acceleration libraries, frameworks), and training / inference algorithms like hybrid parallelism, low precision data formats, sparsity, P / D splitting.Familiarity with AI chip microarchitecture is an asset.Seniority level
Mid-Senior levelEmployment type
Full-timeJob function
Engineering and Information TechnologyIndustries
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