Research Engineer - AI Workload & Systems

Huawei Technologies Canada Co., Ltd.; Markham; On-site; CAD 178,070 - 315,479; Full time

Job summary

Huawei Canada is seeking a Researcher for a 12-month contract to join the Frontier AI Technology Research initiative. The role focuses on hardware-aware AI workload analysis, kernel optimization, and evaluating next-generation accelerators for large-scale model training and inference.

The successful candidate will track evolving AI architectures (LLMs, MoE, sparse/linear attention), develop performance models, and provide architecture guidance to improve data efficiency and system performance in

Qualifications

• Experience with AI accelerator architectures and memory hierarchies.
• Hands-on kernel development using CUDA, Triton, or TileLang.
• Experience with LLM inference systems and serving frameworks like vLLM or SGLang.
• Ability to profile with Roofline-based methodologies.
• Publication or open-source contributions in AI infrastructure are a plus.
• Ph.D. in AI/CS is an asset.

Responsibilities

• Track evolution of state-of-the-art AI model architectures including LLMs, VLMs, MoE.
• Analyze computational characteristics of model architectures and workloads.
• Develop a framework to map AI workloads to hardware requirements.
• Identify patterns, design efficient inference deployment strategies, build performance models.
• Provide architectural recommendations for AI accelerators.

Skills

AI architectures

AI accelerators

Kernel development

CUDA

Triton

PyTorch

JAX

Roofline Profiling

MoE

Education

Ph.D. in AI / CS

Tools

CUDA

Triton

TileLang

PyTorch

JAX

Job description

Huawei Canada has an immediate 12-month contract opening for a 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:

Frontier AI Technology Research

• Track the evolution of state-of-the-art AI model architectures, including Large Language Models (LLMs), Vision Language Models (VLMs), advanced attention mechanisms, and Mixture-of-Experts (MoE) architectures.
• Analyze the computational characteristics of emerging model architectures and Agentic AI training and inference workloads.

Hardware-Oriented Workload Analysis

• Develop a systematic framework to map AI applications and workloads to hardware requirements.
• Identify key computational patterns, design efficient inference deployment strategies, and build analytical performance models.
• Evaluate the impact of algorithmic and hardware innovations on system performance, and provide quantitative, explainable architectural recommendations for next-generation AI accelerators.

The total target annual compensation (based on 2,080 hours per year) for this position ranges from $127,000 to $225,000 depending on education, experience, and demonstrated expertise.

About the ideal candidate：

• Strong understanding of modern AI model architectures and emerging trends, including sparse attention, linear attention, Mixture-of-Experts (MoE), and related techniques.
• Solid knowledge of AI accelerator architectures (e.g., GPUs, TPUs), with deep understanding of memory hierarchy, interconnect technologies, and hardware performance bottlenecks.
• Hands-on experience with AI kernel development using technologies such as Triton, TileLang, CUDA, or equivalent. Strong understanding of FlashAttention and other state-of-the-art kernel optimization techniques.
• Experience with modern LLM inference systems and optimizations, including vLLM, SGLang, or similar serving frameworks. Familiarity with the internals of deep learning frameworks such as PyTorch and JAX.
• Experience using hardware performance analysis and profiling tools, with the ability to develop Roofline-based performance analysis methodologies and tools.
• Ph.D. in Artificial Intelligence, Computer Architecture, Computer Systems, or a closely related field is an asset.
• Demonstrated research contributions through publications or influential open-source projects in AI infrastructure, systems, or computer architecture is an asset.
• Experience deploying and optimizing large-scale AI training or inference systems in production environments is an asset.

Additional Information:

Huawei Canada is committed to a fair, inclusive, and accessible recruitment process. If you require accommodation during any stage of the hiring process, please let us know and we will work with you to meet your needs.

All applications for this position are reviewed directly by our hiring team, we do not use artificial intelligence tools to screen or select candidates.