Associate Director, Model Risk Management

Royal Bank of Canada>; Toronto; On-site; CAD 58,000 - 104,000; Full time

Job summary

Huawei Canada has an immediate 6-12 months internship opening for an Intern Researcher. The position is full-time and focuses on developing and optimizing foundation models (LLM/Code/Multimodal), exploring hardware-aware methods, and building distributed training/inference systems.

The ideal candidate is pursuing a PhD or Master's in CS or EE with exposure to AI architectures, memory systems, and parallelism concepts, and has experience with PyTorch and related tools.

Qualifications

• Pursuing a graduate degree in Computer Science, Electrical Engineering, or related field.
• Strong interest in AI architectures, transformers, and memory systems.
• Experience with deep learning frameworks and distributed training concepts.

Responsibilities

• Assist with model architecture improvements and experimentation for foundation models.
• Support distributed training and inference systems optimization and benchmarking.
• Collaborate with hardware architects and algorithm engineers on R&D tasks.

Skills

Foundation models

PyTorch

Distributed training

Research experience

GPU acceleration

Education

Master's or PhD in CS/EE

Tools

PyTorch

vLLM

SGLang

Job description

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

Full-time

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:

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, 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 2,080 hours per year) ranges from $58,000 to $104,000 depending on education, experience, and demonstrated expertise.

Job requirements

About the ideal candidate:

Currently pursuing a PhD or Master 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.

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.