Principal Machine Learning Research Engineer Foundation Models

Huawei Canada has an immediate permanent opening for a Principal Research 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:

• Lead research and development of large-scale foundation models, optimized for Huawei Ascend GPU-class hardware.

• Drive or contribute to research on advanced algorithms for accelerating the training of market-driven AI models (CV, NLP, GNN, etc.), aiming to achieve or surpass state-of-the-art performance. Develop proofs of concept to validate these methods, which may include—though are not limited to—optimizers, loss functions, novel model architectures, mixed precision, model compression, and emerging learning paradigms (e.g., meta-learning).

• Track emerging AI development in both industry and academia, conduct in-depth insight analysis, and generate research reports for promoting the Ascend ecosystem.

• Partner with global Huawei research centers to advance foundation model capabilities.

About the ideal candidate：

• Master’s or PhD in Computer Science, Mathematics, or Statistics with a focus on AI and Deep Learning, supported by a strong publication record.

• Relevant experience in optimizing deep learning training performance and/or applying models to CV, NLP, or GNN domains.

• Proficient in deep learning frameworks such as TensorFlow, Keras, PyTorch, or MXNet.

• Strong programming expertise in Python and C++.

• Strong presentation and communication skills for both internal and external audiences.

• Skilled in preparing technical documentation, research reports, and academic publications.

• Familiarity with AI accelerators or the broader AI acceleration stack is an advantage.

• Solid mathematical foundation in optimization methods (e.g., gradient descent) is a plus.

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