Senior Researcher - GPU Applications Architecture
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About this role
Huawei Canada has an immediate permanent opening for an Senior Researcher.
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.
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