Senior Machine Learning Research Engineer - Acceleration of AI models

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 is to enhance algorithm performance and training efficiency across industries, fostering long-term competitiveness.

About the job:

1. Track the trend of AI theory and technology development in the world and generate research reports and proposals for promoting Ascend system accordingly.
2. 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 architectures, mix precision, model compression, learning technologies (e.g., meta-learning), etc.
3. 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.
4. Team up with other departments/teams from Huawei’s global research centers for collaboration.
5. Assist the team lead on the planning of projects and definition of technology/products development road map.

About the ideal candidate:

1. Master or PhD in Computer Science, Math/Statistics, focusing on AI & Deep Learning with solid publication records.
2. 2+ years working experience in optimizing performance of training deep learning models and/or their applications to CV/NLP/GNN domains.
3. Solid skills in programming in Tensorflow/Keras/PyTorch/MXNet.
4. Hands-on skills in C++/Python programming.
5. Excellent documentation skills in writing internal reports and/or publishing research papers.
6. Excellent communication skills in internal and external presentation.
7. Working knowledge of AI accelerators or the full stack of AI acceleration system is an asset.
8. Strong math background in optimization (e.g., gradient descent) is an asset.

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