Senior Researcher - GPU Applications Architecture

Overview

Senior Researcher - GPU Applications Architecture at Huawei Canada. This is a permanent opening.

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.

• Conduct in-depth analysis of physical AI, 3
  
  DGS, unmanned driving, and embodied AI application and technological trends. Thoroughly analyze 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.
• Analyze GPU cluster systems. 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, 3
  
  DGS, unmanned driving, embodied AI, and cloud rendering, ensuring industry-leading capability in environmental simulation and rendering.
• Continuously drive technological competitive edge in space intelligence and propel the development of a self-developed GPU technology ecosystem.

• In-depth knowledge about emerging AI and GPU applications, such as physical AI, embodied AI, 3
  
  DGS, 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 systems, 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.

• Mid-Senior level

• Full-time

• Research, Analyst, and Information Technology
• Industries
• Telecommunications