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Huawei Canada has an immediate permanent opening for a Researcher.

About the team

The Software-Hardware System Optimization Lab focuses on research and innovation in power efficiency and performance optimization for consumer devices. By leveraging the talents and capabilities of local academia and our team, we aim to build system-optimization capabilities for software and hardware across edge AI, multimedia, graphics, mobile gaming, and system software domains, thereby enhancing the user experience and performance competitiveness of Huawei's consumer device products.

About the job

• Research and prototype next-generation GPU/AI/hardware architectures — explore the frontier of AI-driven rendering, AI model training/fine-tuning on GPUs, and heterogeneous compute (CPU/GPU/NPU) co-design to shape future product direction.
• Design and build AI compute platforms and toolchains — develop training/fine-tuning support, model deployment, evaluation harnesses, and agent/skill tooling that enable AI teams to move faster.
• Profile and optimize GPU/CPU/AI performance across multiple platforms, driving real, measurable improvements from architecture down to kernel and shader level.
• Build bridges across the stack — collaborate closely with hardware design, software/driver, AI/algorithm, and product/game teams to validate ideas from architecture through to production.

About the ideal candidate

• PhD in a computer-related field (with a focus on AI/ML or computer architecture) plus industry work experience.
• Working knowledge of AI — familiarity with AI frameworks such as PyTorch, TensorFlow, or JAX, and a solid grasp of model training, fine-tuning, and inference workflows even if not at an expert level.
• Proficiency in C/C++ and the ability to balance tradeoffs between architecture, design, and performance.
• A research mindset — comfort exploring uncertain frontiers, validating hypotheses with prototypes, and communicating findings across teams.
• Familiarity with the GPU ecosystem — CUDA, Vulkan, OpenGL, OpenCL, and/or WebGPU — with hands-on experience in at least one of these and the ability to reason about GPU microarchitecture and performance.
• Familiarity with hardware architecture with hands-on practical experience, understanding how both graphics (rendering/games) and AI (training/inference) workloads shape hardware design, memory, and compute decisions.
• Experience building AI agent / harness / skill toolchains, including model evaluation, orchestration, and LLM-powered tooling.

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