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Co-op Software Engineer - AI System & Infrastructure

Vancouver, BC, Canada

The target annual compensation (based on 2080 hours per year) ranges from $58,000 to $104,000 depending on education, experience and demonstrated expertise. · Internship

Within 25 km of Delta · About 19.6 km from the city centre

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Job details

Pay: The target annual compensation (based on 2080 hours per year) ranges from $58,000 to $104,000 depending on education, experience and demonstrated expertise.
Job type: Internship
Posted: May 12, 2026

Original job description

Huawei Canada has an immediate Co-op opening for an Engineer.

About the team:

The Intelligent Cloud Infrastructure Lab aims to innovate technologies, algorithms, systems, and platforms for next-generation cloud infrastructure. The lab addresses scalability, performance, and resource utilization challenges in existing cloud services while preparing for future challenges with appropriate technologies and architectures. Additionally, the lab aims to understand industry dynamics and technology trends to create a robust ecosystem.

About the job:

Understand AI System and Infrastructure technology landscape, and identify scalability/performance issues or challenges of current LLM/multi-modal LLM systems
Initiate and charter innovation projects to build or re-architect AI infrastructure platform, and plan milestones accordingly
Provide/contribute a scalable and high-performance architecture design or re-design for the infrastructure system that is optimized for AI training and inferencing, which includes but not limited to cluster management and scheduling, LLM model deployment, elastic LLM as well as AI container cold/warm start-up optimization, and so on.
Collaborate with internal and external teams to deliver the project or project features that improve our overall system scalability and performance.

The target annual compensation (based on 2080 hours per year) ranges from $58,000 to $104,000 depending on education, experience and demonstrated expertise.

Job requirements

About the ideal candidate:

Bachelors, Master/PhD degree in Computer Science, Computer Engineering
Experience in building large scale and high-performance distributed system
Experience in Nvidia TensorRT and/or Triton servers. Experience in container virtualization technologies
Knowledge & experience in distributed system design & development, including serverless technologies
Work experience in one or more of the following technologies: vLLM, Ray, SGLang, Kubernetes, TensorRT-LLM, Pytorch framework, Cuda libraries, GPU technologies
Work experience in one or more of the following programming languages: C/C++, Go, Java, Rust, python, C#
Have excellent interpersonal and communication skills to collaborate with multiple teams and build strong partnerships effectively
Demonstrated success working on software engineering problems that span multiple products

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More about this job

Responsibilities

The role involves understanding the AI System and Infrastructure technology landscape and identifying scalability and performance challenges. The engineer will initiate innovation projects to build or re-architect AI infrastructure platforms and collaborate with teams to improve system scalability and performance.

Requirements

The ideal candidate should have a Bachelor's, Master's, or PhD degree in Computer Science or Computer Engineering and experience in building large-scale distributed systems. Familiarity with technologies such as Nvidia TensorRT, container virtualization, and programming languages like C/C++, Python, and Java is also required.

Skills

Distributed System Design
Container Virtualization
Nvidia TensorRT
Triton Servers
Serverless Technologies
Kubernetes
Pytorch Framework
Cuda Libraries
GPU Technologies
C/C++
Go
Java
Rust
Python
C#

Visa sponsorship

Not detected in the job text

Keywords

AI
Infrastructure
Cloud
Scalability
Performance
Resource Utilization
Distributed Systems
Nvidia TensorRT
Triton Servers
Container Virtualization
Serverless Technologies
Kubernetes
Pytorch
Cuda
GPU
C/C++
Go
Java
Rust
Python
C#
LLM
Multi-modal LLM
Cluster Management
Scheduling
Model Deployment
Elastic LLM
AI Container Optimization
Collaboration
Engineering

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