Principal Architect - AI Workload & Architecture Intelligence

Job Description

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. It also develops next-generation GPU architecture for gaming, cloud rendering, VR/AR, and Metaverse applications.

One of the goals of this lab is to enhance algorithm performance and training efficiency across industries, fostering long-term competitiveness.

About the job:

1. Analyze the evolution trends of AI technologies. Research emerging AI architectures such as World Models, Agents, and Multimodal Foundation Models, as well as the computational characteristics of autonomous intelligent systems like AutoGPT and AI Agents. Study workload characteristics of next-gen transformer architectures (e.g., MoE, SSM) and track new applications like AI+ scientific computing to ensure the accuracy and advancement of AI architecture evolution.

2. Conduct hardware-oriented workload analysis: establish a framework for mapping AI applications to hardware requirements, extract key computing patterns, and translate them into hardware design requirements. Provide architecture recommendations for customized AI accelerators and assess the impact of new storage and interconnect architectures on AI performance.

3. Offer system-level optimization suggestions: propose system architecture improvements based on workload analysis, design customized acceleration solutions for specific AI applications, and assist in developing technology roadmaps for chips and systems. Continuously prepare hardware architectures and systems for future development needs of mainstream AI applications.

The base salary for this position ranges from $190,000 to $350,000 depending on education, experience, and demonstrated expertise.

About the ideal candidate:

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

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

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

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

5. PhD in AI architecture, computer architecture, or related fields is preferred.

6. Solid publication record in AI systems or chip design.

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

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