Researcher – Agent Platform R&D

Huawei Canada has an immediate permanent opening for a Researcher.

About the team:

The Distributed Data Storage and Management Lab leads research in distributed data systems, aiming to develop next-generation cloud serverless products that encompass core infrastructure and databases. This lab addresses various data challenges, including cloud-native disaggregated databases, pay-by-query user models, and optimizing low-level data transfers via RDMA. Teams within this lab create advanced cloud serverless data infrastructure and implement cutting-edge networking technologies for Huawei's global AI infrastructure.

About the job:

• Participate in the core design and development of our proprietary open-source agent framework, including architectural evolution, module abstraction, toolchain construction, and developer experience optimization.
• Deeply track and technically dissect emerging agent projects such as OpenClaw and Hermes, understand their design philosophies, capabilities, and innovations, and integrate effective ideas into our framework and products.
• Design and implement mainstream and cutting-edge agent architectures, exploring advanced mechanisms such as self-evolution and multi-agent collaboration.
• Drive the deployment of the Agent framework in real-world toB and toC products, extracting requirements from developer feedback and business scenarios to continuously improve the framework.

The total target annual compensation for this position ranges from $106,000 to $156,000 depending on education, experience, and demonstrated expertise.

About the ideal candidate:

• Master’s degree or higher (or equivalent practical experience) in Computer Science, Artificial Intelligence, Machine Learning, or related fields.
• Solid programming skills, proficiency in Python, familiarity with asynchronous programming, type systems, and engineering best practices. Experience maintaining or contributing to open-source projects is a plus.
• Deep understanding of the principles and usage of large language models (GPT, Claude, Qwen, LLaMA, etc.), including prompt engineering, context management, function calling, etc.
• Familiarity with at least one agent framework (LangChain, AutoGen, CrewAI, etc.) and the ability to critically analyze its architectural strengths and weaknesses.