Principal Architect - AI Workload & Architecture Intelligence

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:

• Analyze the evolution trend of AI technologies. Research on emerging AI architectures such as World Models, Agents, and Multimodal Foundation Models, computational characteristics of autonomous intelligent systems such as AutoGPT and AI Agents, and workload characteristics of next-gen transformer architectures (such as MoE, SSM, etc.). Track new applications, such as AI+ scientific computing, to ensure the accuracy and advancement of AI architecture evolution.

• Hardware-oriented workload analysis: Establish a framework for mapping AI applications to hardware requirements. Extract key computing patterns and convert them into hardware design requirements. Provide architecture recommendations for customized AI accelerators. Assess the impact of new storage and interconnect architectures on AI performance.

• System-level optimization suggestions: Provide system architecture improvement suggestions based on workload analysis. Design customized acceleration solutions for specific AI applications. Assist in developing technology roadmaps for chips and systems. Continuously prepare hardware architectures and systems for future development requirements of mainstream AI applications

  stems.

About the ideal candidate：

• Proficiency in the latest AI model architecture (such as World Models, MoE, and Agents). Deep understanding of AI algorithm mechanisms.

• Familiarity with AI chip architecture (such as GPU, NPU, and TPU). Understanding of memory hierarchy and interconnect technologies. Proven experience in system architecture design.

• Solid command of the underlying implementation of AI frameworks (such as PyTorch and JAX).

• Familiarity with hardware performance analysis tools. Experience in cluster architecture design and optimization using simulators is preferred.

• PhD preferred in AI architecture, computer architecture, or related fields.

• Solid publication records in the field of AI systems or chip design.

• Experience in deploying large-scale AI systems is an asset.   

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