Senior Researcher – Hardware Efficient AI Foundation Model Training
About the role
Huawei Canada has an immediate permanent opening for a Principal Architect. 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:
- Collaborate with internal and external organizations to lead the design of foundational model architecture for LLM/Code/Multimodal subfields by breakthroughs in post-training and continual training. Develop a foundational model with state-of-the-art performance and hardware efficiency, and establish industry impact.
- Propose the technical requirements for large-scale distributed training and inference infrastructures such as parallelization and operator fusion, analyze the computational characteristics of typical architectures, and ensure the accuracy and advancement of AI hardware & infrastructure evolution.
Job requirements
About The Ideal Candidate：
- Experience in training and optimizing cutting-edge AI models/applications, especially in training and deploying AI models at a scale of 10B+ parameters.
- Proficiency in the latest AI architecture (such as long-sequence, reinforcement learning, multimodal, and agents). Deep understanding of AI algorithm mechanisms.
- Solid command of the underlying implementation of AI frameworks (such as PyTorch, vLLM, and SGLang), and mainstream distributed training and inference techniques.
- Familiarity with AI chip architecture (such as GPU, NPU, and TPU). Understanding of memory hierarchy and interconnect technologies is an asset.
- PhD preferred in AI architecture, computer architecture, or related fields.
- Solid publication records in the field of AI systems or chip design are an asset.
About Huawei Canada
Founded in 1987, Huawei is a leading global provider of information and communications technology (ICT) infrastructure and smart devices. We are committed to bringing digital to every person, home and organization for a fully connected, intelligent world. We have approximately 197,000 employees and we operate in over 170 countries and regions, serving more than three billion people around the world.
In Canada, Huawei conducts innovative and leading edge research in 5G technologies, along with advanced development of emerging cloud, device and network technologies & services. While our renowned Canada Research Centre in the thriving technology landscape of Ottawa, Ontario continues to grow rapidly in size and strategic product initiatives, additional presence has also been established across Canada with R&D facilities in Vancouver, Edmonton, Waterloo, Markham, Montreal, and a R&D office in Quebec City.
Senior Researcher – Hardware Efficient AI Foundation Model Training
About the role
Huawei Canada has an immediate permanent opening for a Principal Architect. 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:
- Collaborate with internal and external organizations to lead the design of foundational model architecture for LLM/Code/Multimodal subfields by breakthroughs in post-training and continual training. Develop a foundational model with state-of-the-art performance and hardware efficiency, and establish industry impact.
- Propose the technical requirements for large-scale distributed training and inference infrastructures such as parallelization and operator fusion, analyze the computational characteristics of typical architectures, and ensure the accuracy and advancement of AI hardware & infrastructure evolution.
Job requirements
About The Ideal Candidate：
- Experience in training and optimizing cutting-edge AI models/applications, especially in training and deploying AI models at a scale of 10B+ parameters.
- Proficiency in the latest AI architecture (such as long-sequence, reinforcement learning, multimodal, and agents). Deep understanding of AI algorithm mechanisms.
- Solid command of the underlying implementation of AI frameworks (such as PyTorch, vLLM, and SGLang), and mainstream distributed training and inference techniques.
- Familiarity with AI chip architecture (such as GPU, NPU, and TPU). Understanding of memory hierarchy and interconnect technologies is an asset.
- PhD preferred in AI architecture, computer architecture, or related fields.
- Solid publication records in the field of AI systems or chip design are an asset.
About Huawei Canada
Founded in 1987, Huawei is a leading global provider of information and communications technology (ICT) infrastructure and smart devices. We are committed to bringing digital to every person, home and organization for a fully connected, intelligent world. We have approximately 197,000 employees and we operate in over 170 countries and regions, serving more than three billion people around the world.
In Canada, Huawei conducts innovative and leading edge research in 5G technologies, along with advanced development of emerging cloud, device and network technologies & services. While our renowned Canada Research Centre in the thriving technology landscape of Ottawa, Ontario continues to grow rapidly in size and strategic product initiatives, additional presence has also been established across Canada with R&D facilities in Vancouver, Edmonton, Waterloo, Markham, Montreal, and a R&D office in Quebec City.