Principal Scientist - Software/Hardware Co-design, Markham

Principal Scientist - Software/Hardware Co-design

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Principal Scientist - Software/Hardware Co-design

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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 level Employment type

Full-time Job function

Engineering and Information Technology Industries

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