Co-op Software Engineer - AI System & Infrastructure
Huawei Technologies Canada Co., Ltd. Open · verified Oct 24, 2026 At a glance
Junior Software Engineering role at Huawei Technologies Canada Co., Ltd. Vancouver · internship · CA$58,000–CA$104,000 base.
techroles summary, based on the employer's posting.
What you'll do
- Assess AI infrastructure and current large language or multimodal systems to find scaling and performance bottlenecks
- Set up innovation projects, reshape infrastructure architecture and map out milestones
- Design infrastructure for AI training and inference, including cluster scheduling, model rollout and container startup
- Coordinate with internal and external teams to deliver changes that improve system scale and speed
What you bring
- A computer science or computer engineering degree at bachelor’s level or above
- Experience building high-performance distributed systems at scale and designing or developing distributed software
- Hands-on experience with NVIDIA TensorRT or Triton Server, plus container virtualization
- Experience with at least one listed AI, infrastructure or programming technology, and software problems spanning multiple products
Who this fits
This co-op suits a junior software engineer with distributed-systems experience who can collaborate across teams. The role is onsite in Vancouver.
From the employer
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.
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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