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:
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Lead the architecture design of Ascend training products, driving the
continuous evolution of architectural competitiveness.
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Analyze mainstream scenario requirements and industry technology trends for
Ascend, introducing innovative technologies to ensure sustained leadership in
architectural competitiveness.
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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.
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Collaborate with other departments/teams from Huawei’s global research
centers to align on strategic goals
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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:
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Master’s or PhD in Computer Science, Math/Statistics, with a focus on AI &
Deep Learning.
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5+ years of experience in architecting large-scale AI training systems or
similar complex software-hardware integrated solutions.
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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.
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Working knowledge of AI accelerators or full-stack AI acceleration systems
and Deep Reinforcement Learning.
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Hands-on experience with veRL or Ray for large-scale model training.
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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.
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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.
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Strong programming skills with proficiency in C/C++ and Python.
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Experience using performance analysis tools such as Nsight Systems, Nsight
Compute, and DLProf.
