Senior Principal Researcher - AI for Science

Job description

Huawei Canada has an immediate permanent opening for a Senior Principal Researcher - AI4Science

About the team :

The Technology Planning and Cooperation Department promotes strategic innovation across all of Huawei Canada. Working with fellow experts, the focus is researching new and upcoming areas of technology as well as strategic planning to help achieve long term vision of Huawei. This department offers a unique opportunity to leverage both technical and business skills.

While we are developing next-generation computing infrastructure and AI foundation models that integrate physics, simulation, and learning at unprecedented scale, this is the position we are targeting to bring in a bright individual who bridges scientific AI research and system-level innovation—advancing our mission of combining computing and connectivity to enable large-scale AI for Science and simulation intelligence.

About the job :

Conduct cutting-edge research at the intersection of Scientific computation and AI infrastructure, including physics-informed AI, distributed training, and hybrid simulation–ML workflows.

Collaborate across Huawei’s Computation + networking R&D divisions to design infrastructure-aware AI models and co-optimize algorithms and hardware.

Lead or contribute to AI4Science foundation models in areas such as materials, physics, molecular, or multi-scale simulation.

Prototype distributed training solutions and integrated AI pipelines using Huawei’s MindSpore and Ascend AI ecosystems.

Publish research in top-tier ML and science venues (e.g., NeurIPS, ICML, JHEP, PRD) and contribute to Huawei’s internal IP and R&D assets.

Mentor junior researchers and contribute to long-term strategic planning of Huawei’s AI4Sci + Compute Infrastructure initiatives.

Job requirements

About the ideal candidate :

Ph.D. in Physics, Computer Science, Computational Science, or a related field.

Proven research experience in AI for Science, scientific simulation, or physics-informed learning.

Strong programming and distributed system experience (Python, CUDA, PyTorch, TensorFlow).

Familiarity with HPC, parallel computing, or large-scale AI infrastructure.

Strong research record demonstrated through publications or impactful project experience.