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    Senior Machine Learning Research Engineer - Markham, Canada - Huawei Technologies Canada Co., Ltd.

    Huawei Technologies Canada Co., Ltd. background
    Full time
    Description

    Our team has an immediate permanent opening for a Senior Machine Learning Research Engineer.
    Responsibilities:

    • 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.


    What you'll bring to the team:

    • Master/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.

    #LI-SZ2


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