Technical VP – GPU Applications Architecture

Huawei Canada has an immediate permanent opening for a Technical VP .

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. The 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 :

Conduct in-depth analysis of physical AI, 3DGS, unmanned driving, and embodied AI applications and technological trends. Analyze key requirements for GPU computing architecture. Plan environmental simulation and rendering platforms based on self-developed GPU architecture, providing development, training, and simulation capabilities for emerging applications.

Analyze GPU cluster systems. Define hardware architecture, system framework, and software architecture. Implement large-scale cluster intelligent solutions. Support development and simulation of emerging applications such as physical AI, 3DGS, unmanned driving, embodied AI, and cloud rendering, ensuring industry-leading capabilities in environmental simulation and rendering.

Drive technological competitiveness in space intelligence and develop a self-developed GPU technology ecosystem.

About the ideal candidate :

Deep knowledge of emerging AI & GPU applications, such as physical AI, embodied AI, 3DGS, and World Models. Experience in large-scale projects like autopilot and robot simulation is a plus.

Knowledge of AI-enhanced rendering technologies / algorithms, such as DLSS and Neural Rendering. Experience with rendering engines (UE and Unity) is a plus.

Familiarity with AI chip architecture, computer architecture, operating systems, and distributed reasoning & training frameworks. Proven experience in designing system architectures.

Proficiency in GPU architecture, programming models, languages including CUDA, OpenCL, Vulkan, and APIs.

Expertise in GPU performance profiling and tuning. Ability to develop and use analysis and performance enhancement tools.

Strong teamwork and communication skills for effective project collaboration.

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