Technical VP – GPU Applications Architecture

7 days ago

Markham, Canada Huawei Full time

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. Additionally, this team 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, and provide development, training, and simulation capabilities for emerging applications.

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

- 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 with large-scale projects like autopilot and robot simulation is a plus.

- Expertise in AI-enhanced rendering technologies/algorithms, such as DLSS and Neural Rendering. Experience with rendering engines like Unreal Engine and Unity is beneficial.

- Familiarity with AI chip architecture, computer architecture, operating systems, and distributed reasoning & training frameworks. Proven system architecture design experience.

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

- Expertise in GPU performance profiling and tuning, with the ability to develop tools for analysis and performance improvement.

- Strong teamwork and communication skills for effective collaboration on projects.

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