Senior Machine Learning Research Engineer - Acceleration of AI models

Huawei Technologies Canada Co., Ltd.; Markham; On-site; CAD 80,000 - 130,000; Full time

Job summary

An established industry player is seeking a Senior Machine Learning Research Engineer to join their innovative team in Markham. This role focuses on accelerating AI models through cutting-edge research and collaboration across global research centers. The ideal candidate will have a strong academic background in Computer Science or Mathematics, with hands-on experience in optimizing deep learning models. You will contribute to groundbreaking projects that enhance algorithm performance and efficiency, while also having the opportunity to publish research and present at conferences. Join a forward-thinking company and make a significant impact in the field of AI.

Qualifications

• Master or PhD in Computer Science or Math/Statistics focusing on AI & Deep Learning.
• 2+ years in optimizing training of deep learning models in CV/NLP/GNN.

Responsibilities

• Lead research on algorithms for accelerating AI model training.
• Publish AI research papers and file patents on critical algorithms.

Skills

Deep Learning

AI Theory

Algorithm Optimization

Communication Skills

Documentation Skills

Education

Master in Computer Science

PhD in Computer Science

Tools

TensorFlow

Keras

PyTorch

MXNet

C++

Python

Job description

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Senior Machine Learning Research Engineer - Acceleration of AI models, Markham

Location: Markham, Canada

Job Category:

Information Technology

Job Reference:

w46lenyw

Job Views:

Posted:

Expiry Date:

17.04.2025

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 is 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 reports and proposals for promoting the Ascend system accordingly.
• Lead or participate in research of algorithms in accelerating the training of market-driven AI models (CV/NLP/GNN/…), reaching/exceeding state-of-the-art accuracy, and develop a proof of concept of the algorithms. These algorithms include but are not limited to optimizers, loss functions, new model architecture, mixed precision, model compression, learning technologies (e.g., meta-learning), etc.
• Publish relevant high-quality AI research papers when necessary and approved, and attend conferences to increase public awareness of Huawei’s Ascend products; file high-value patents on critical algorithms/processes that have potential business gain.
• Team up with other departments/teams from Huawei’s global research centers for collaboration.
• Assist the team lead in 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 of 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 presentations.
• Working knowledge of AI accelerators or the full stack of AI acceleration systems is an asset.
• Strong math background in optimization (e.g., gradient descent) is an asset.