Senior Engineer - Large Model and Training System Performance Optimization

Huawei Canada has an immediate permanentopening for a Senior Engineer.

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 :

Track the trend of AI theory and technology development in the world and generate research report and proposals for promoting Ascend system accordingly.

Lead or participate in research of algorithms in accelerating the training of the market-driven AI models (CV / NLP / GNN / …), reaching / exceeding the state of the art accuracy, and develop a proof of concept of the algorithms. Those algorithms include but are not limited to the following : optimizers, loss functions, new model architecture, mix precision, model compression, learning technologies (e.g., meta-learning), etc.

Publish relevant high-quality AI research papers when necessary and approved, and attend conferences for increasing public awareness of Huawei’s Ascend products; file high-value patents on critical algorithms / processes that are of potential business gain.

Team up with other departments / teams from Huawei’s global research centers for collaboration.

Assist the team lead on theplanning of projects and definition of technology / products development road map.

The base salary for this position ranges from $121,000 to $230,000 depending on education, experience and demonstrated expertise.

About the ideal candidate：

Master’s or PhD in Computer Science, Math / Statistics, with a focus on AI & Deep Learning.

2+ years of working experience in optimizing the performance of training deep learning models and / or their applications in domains such as CV, NLP, or GNN. A proactive attitude with a strong ability to tackle challenges and adapt to evolving requirements and dynamic work environment

Excellent documentation skills for writing internal reports and / or publishing research papers. Effective communication skills for presentations to internal and external audiences.

Working knowledge of AI accelerators or full-stack AI acceleration systems and Deep Reinforcement Learning.

Hands-on experience with veRL or Ray for large-scale model training.

Familiarity with processor architectures and relevant work experience, with hands-on expertise in designing and developing complex system software architectures, and experience in performance optimization on GPU / NPU or similar hardware platforms.

Solid understanding of deep learning fundamentals, proficiency with the PyTorch framework, and practical experience in performance optimization using upper-layer distributed frameworks such as Megatron or DeepSpeed.

Strong programming skills with proficiency in C / C++ and Python.

Experience using performance analysis tools such as Nsight Systems, Nsight Compute, and DLProf.

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