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:
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Track the trend of AI theory and technology development in the world and
generate research report and proposals for promoting Ascend system
accordingly.
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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.
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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.
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Team up with other departments/teams from Huawei’s global research centers
for collaboration.
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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:
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Master’s or PhD in Computer Science, Math/Statistics, with a focus on AI &
Deep Learning.
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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
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Excellent documentation skills for writing internal reports and/or publishing
research papers. Effective communication skills for presentations to internal
and external audiences.
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Working knowledge of AI accelerators or full-stack AI acceleration systems
and Deep Reinforcement Learning.
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Hands-on experience with veRL or Ray for large-scale model training.
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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.
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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.
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Strong programming skills with proficiency in C/C++ and Python.
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Experience using performance analysis tools such as Nsight Systems, Nsight
Compute, and DLProf.
