Senior Machine Learning Research Engineer

Job description

Huawei Canada has an immediate permanent opening for a Senior 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:

• 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.

About the ideal candidate：

• Master or PhD in Computer Science, Math/Statistics, focusing on AI & Deep Learning with solid publication records.

• 2+ years working experience in optimizing performance of training deep learning models and/or their applications to CV/NLP/GNN domains.

• Solid skills in programming in Tensorflow/Keras/PyTorch/MXNet.

• Hands-on skills in C++/Python programming.

• Excellent documentation skills in writing internal reports and/or publishing research papers.

• Excellent communication skills in internal and external presentation.

• Working knowledge of AI accelerators or the full stack of AI acceleration system is an asset.

• Strong math background in optimization (e.g., gradient descending) is an asset.