Principal Architect - Large Model and Training System Performance Optimization

Huawei; Vancouver; On-site; USD 121,000 - 230,000; Full time

Job summary

An innovative firm is seeking a Principal Architect to lead the design of cutting-edge AI training systems. In this pivotal role, you will drive architectural competitiveness and collaborate with global teams to enhance algorithm performance. The ideal candidate will possess a Master's or PhD in Computer Science or Math/Statistics and have extensive experience in architecting large-scale AI solutions. This role offers an exciting opportunity to shape the future of AI technologies and contribute to groundbreaking projects in a dynamic environment, making a significant impact on the industry. If you are passionate about AI and eager to tackle complex challenges, this position is perfect for you.

Qualifications

• 5+ years of experience in architecting large-scale AI training systems.
• Master’s or PhD in Computer Science or Math/Statistics required.

Responsibilities

• Lead architecture design of Ascend training products and drive innovation.
• Collaborate with global teams to align on strategic goals.

Skills

AI & Deep Learning

Architecting large-scale AI training systems

C/C++ programming

Python programming

Documentation skills

Communication skills

Performance optimization

Deep Reinforcement Learning

Education

Master’s or PhD in Computer Science

Master’s or PhD in Math/Statistics

Tools

veRL

Ray

Nsight Systems

Nsight Compute

DLProf

PyTorch

Megatron

DeepSpeed

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. This team 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:

• 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.