Intern Researcher – AI Foundation Model Training

Huawei Technologies Canada Co., Ltd.; Markham; On-site; CAD 58,000 - 104,000; Full time

Job summary

Huawei Technologies Canada Co., Ltd. in Markham is offering a 6-12 month internship for an Intern Researcher. The role involves developing AI models, optimizing data efficiency, and collaborating with cross-functional teams in a cutting-edge research environment.

The ideal candidate is pursuing a PhD or Master's in Computer Science or Electrical Engineering, with strong interests in AI architectures and distributed training. Compensation ranges from $58,000 to $104,000 annually, depending on experience and education.

Qualifications

• Currently pursuing a PhD or Master's Degree with exposure to AI architectures.
• Familiarity with distributed training concepts is essential.
• Demonstrated interest in research through projects, internships, or publications.

Responsibilities

• Support the development of foundation models and model architecture improvements.
• Assist in building and optimizing distributed training and inference systems.
• Collaborate with cross-functional teams on research and development tasks.

Skills

Deep learning frameworks (e.g., PyTorch, vLLM, SGLang)

Large-scale model training concepts

Distributed training fundamentals

Hardware-aware model optimization

Multimodal model development

Education

PhD or Master’s Degree in Computer Science or Electrical Engineering

Tools

GPU / NPU / TPU

Job description

Huawei Canada Internship – Intern Researcher

Huawei Canada has an immediate 6-12 month internship opening for an Intern Researcher.

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

• Support the development and optimization of foundation models (LLM / Code / Multimodal) by assisting with model architecture improvements and experimentation, contributing to post‑training optimization techniques, supporting research on continual learning approaches, and exploring hardware‑aware methods to improve model efficiency.
• Assist in building and optimizing distributed training and inference systems: learn and apply parallelization strategies (model / tensor / data parallelism), support operator‑level and computational graph optimizations, contribute to performance benchmarking and analysis.
• Collaborate with cross‑functional teams: work closely with hardware architects and algorithm engineers on research and development tasks and support experiments and prototyping to improve system and model performance.

The total target annual compensation (based on 2,080 hours per year) ranges from $58,000 to $104,000 depending on education, experience, and demonstrated expertise.

About the ideal candidate

• Currently pursuing a PhD or Master’s Degree in Computer Science, Electrical Engineering, or a related field, with exposure to areas such as AI architectures (e.g., transformers, mixture‑of‑experts), Computer architecture (e.g., memory systems, interconnects) or related domains.
• Familiarity with one or more of the following: Deep learning frameworks (e.g., PyTorch, vLLM, SGLang), large‑scale model training concepts, distributed training fundamentals (e.g., data/model/tensor parallelism).
• Demonstrated interest in research and development through academic projects, internships, or publications.
• Technical interests (experience in at least one is a plus): Multimodal model development, Reinforcement learning, Long‑context modeling, AI agent systems, hardware‑aware model optimization or neural architecture search.
• Preferred qualifications: Hands‑on experience with GPUs or AI accelerators (e.g., GPU / NPU / TPU), contributions to academic or open‑source ML projects, prior internship or research experience in AI/ML systems.

Additional Information

Huawei Canada is committed to a fair, inclusive, and accessible recruitment process. If you require accommodation during any stage of the hiring process, please let us know and we will work with you to meet your needs.

All applications for this position are reviewed directly by our hiring team, we do not use artificial intelligence tools to screen or select candidates.