Huawei Canada has a 12-month contract opening for a Senior Research Engineer.
About the team:
Established in 2014, the Distributed Scheduling and Data Engine Lab is Huawei Cloud's technical innovation center in Canada. The lab focuses on researching and developing advanced cloud technologies, supporting the productization and iterative optimization of its technical achievements. Current research areas include cloud native databases, intelligent SQL engine, AI/Agent infrastructure and LLM/Agent Evaluation Technology. The lab fosters a robust technical environment, allowing collaboration with industry experts to create a highly competitive cloud platform.
About the job:
Build LLM-powered AI agents for cloud services, focusing on robust agent design, orchestration, and integration into PaaS platforms.
Translate high-level academic concepts into hands-on, production-ready code, building AI tooling and application platforms at scale.
Work closely with a team of AI researchers and cloud engineers to design, implement, and evaluate AI-driven systems.
Focus on the performance and stability of agentic workflows through rigorous testing and system evaluation.
Conduct applied research with opportunities to publish, patent, and push the state of the art in autonomous AI systems.
The total target annual compensation (based on 2,080 hours per year) ranges from
$127,000 to $225,000 depending on education, experience, and demonstrated expertise.
About the Ideal Candidate:
Master’s or PhD in Computer Science, Machine Learning, AI, or a related field; industry experience for PhD holders is an asset.
Strong skills in Python and PyTorch; AI Expertise: Deep understanding of Transformer architectures and Generative AI techniques (fine-tuning, PEFT).
Design and implement sophisticated orchestration layers (State Machines, DAGs, or Multi-agent systems) that go beyond simple linear chains. In addition, develop rigorous benchmarking and evaluation protocols for agents (e.g., trajectory analysis, G-Eval, or custom "LLM-as-a-judge" metrics) to quantify agent reliability.
Familiarity with AI Agent evaluation or observability is an asset; ability to translate high-level conceptual topics into clean, production-ready code.
Excellent communication skills with the ability to work independently on research tasks while maintaining a team-first attitude.
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.
Skills Required
- Master's or PhD in Computer Science, Machine Learning, AI, or a related field
- Industry experience for PhD holders
- Strong skills in Python and PyTorch
- Deep understanding of Transformer architectures and Generative AI techniques
- Ability to design and implement sophisticated orchestration layers
- Familiarity with AI Agent evaluation or observability
- Excellent communication skills
What We Do
Founded in 1987, Huawei is a leading global provider of information and communications technology (ICT) infrastructure and smart devices. We are committed to bringing digital to every person, home and organization for a fully connected, intelligent world. We have approximately 197,000 employees and we operate in over 170 countries and regions, serving more than three billion people around the world. In Canada, Huawei conducts innovative and leading edge research in 5G technologies, along with advanced development of emerging cloud, device and network technologies & services. While our renowned Canada Research Centre in the thriving technology landscape of Ottawa, Ontario continues to grow rapidly in size and strategic product initiatives, additional presence has also been established across Canada with R&D facilities in Vancouver, Edmonton, Waterloo, Markham, Montreal, and a R&D office in Quebec City.

.png)







