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. 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:
-
Conduct in-depth analysis of physical AI, 3DGS, unmanned driving, and
embodied AI application and technological trends, thoroughly analyzing key
requirements for GPU computing architecture. Plan the environmental
simulation and rendering platform based on self-developed GPU architecture,
and provide development, training, and simulation capabilities for emerging
applications.
-
Conduct in-depth analysis of GPU cluster system. Define hardware
architecture, system framework, and software architecture. Implement
large-scale cluster intelligent solutions. Support the development and
simulation of emerging applications such as physical AI, 3DGS, unmanned
driving, embodied AI, and cloud rendering, and ensure the industry-leading
capability in environmental simulation and rendering.
-
Continuously drive technological competitive edge in space intelligence and
propel the development of self-developed GPU technology ecosystem.
About the ideal candidate:
-
In-depth knowledge about emerging AI&GPU applications, such as physical AI,
embodied AI, 3DGS, World Models, etc. Experience in developing large-scale
projects such as autopilot simulation and robot simulation is an asset.
-
In-depth knowledge about AI-enhanced rendering technologies/algorithms, such
as DLSS and Neural Rendering. Experience with rendering engines (UE and
Unity) is an asset.
-
Familiarity with AI chip architecture, computer architecture and operating
system, and distributed reasoning & training framework. Proven experience in
designing system architectures.
-
Proficiency in GPU architecture and programming models, GPU programming
languages including CUDA, OpenCL, and Vulkan, and APIs.
-
Expertise in GPU performance profiling and tuning. Ability to develop and
utilize tools for thorough analysis and performance enhancement.
-
Strong teamwork and communication skills, enabling effective collaboration
with team members to successfully execute projects.
