Senior Engineer - Large Model and Training System Performance Optimization

Huawei Canada has an immediate permanent opening 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 the planning 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.