Huawei Canada has an immediate permanent opening for a Principal Architect.
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:
Lead the architecture design of Ascend training products, driving the continuous evolution of architectural competitiveness.
Analyze mainstream scenario requirements and industry technology trends for Ascend, introducing innovative technologies to ensure sustained leadership in architectural competitiveness.
Identify requirements for MindX, AI frameworks, acceleration libraries, and chip hardware, building a robust software-hardware architecture for Ascend training to achieve ongoing commercial success.
Collaborate with other departments/teams from Huawei’s global research centers to align on strategic goals
Spearhead project planning and define the technology/product development roadmap to guide long-term innovation
The base salary for this position ranges from $121,000 to $230,000 depending on education, experience and demonstrated expertise.
About the ideal candidate：
Master’s or PhD in Computer Science, Math/Statistics, with a focus on AI & Deep Learning.
5+ years of experience in architecting large-scale AI training systems or similar complex software-hardware integrated solutions.
Excellent documentation skills for writing internal reports and/or publishing research papers. Effective communication skills for presentations to internal and external audiences. A proactive attitude with a strong ability to tackle challenges and adapt to evolving requirements and dynamic work environment.
Working knowledge of AI accelerators or full-stack AI acceleration systems and Deep Reinforcement Learning.
Hands-on experience with veRL or Ray for large-scale model training.
Familiarity with processor architectures and relevant work experience, with hands-on expertise in designing and developing complex system software architectures, and experience in performance optimization on GPU/NPU or similar hardware platforms.
Solid understanding of deep learning fundamentals, proficiency with the PyTorch framework, and practical experience in performance optimization using upper-layer distributed frameworks such as Megatron or DeepSpeed.
Strong programming skills with proficiency in C/C++ and Python.
Experience using performance analysis tools such as Nsight Systems, Nsight Compute, and DLProf.
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