Senior Researcher - AI Data Platforms
Huawei Technologies Canada Co., Ltd. Open · verified Oct 22, 2026 At a glance
Senior AI Research role at Huawei Technologies Canada Co., Ltd. Markham · full-time · CA$127,000–CA$225,000 base.
aimlroles summary, based on the employer's posting.
What you'll do
- Research AI data storage and retrieval, including vector-native lakes, intelligent caching and infrastructure for RAG
- Build prototypes for agent memory, knowledge bases and retrieval, advancing hybrid search, indexing and fresh context
- Create data and metadata components with lineage, versioning and lifecycle controls across storage, lakes and vector indexes
- Improve distributed data movement, checkpointing, cache loading and metadata access for large-scale AI systems
- Publish research on agent memory, RAG and AI data management, while contributing to patents and technical forums
What you bring
- A master's or doctoral degree in computer science or a related field, with a strong research record
- First-author papers at leading conferences demonstrating deep work in RAG, agent memory, vector databases or indexing
- Strong prototyping ability with C/C++ and Python, plus experience with distributed storage APIs, processing frameworks or vector indexing libraries
- Knowledge of storage architectures and retrieval trade-offs, including hybrid search, index freshness, and dense versus sparse methods
Who this fits
This senior research role suits someone who can connect academic research with production systems and communicate across hardware, software and AI disciplines. The position is full-time and on-site in Markham, Canada.
From the employer
Huawei Canada has an immediate permanent opening for a Senior Researcher.
About the team:
The Emerging Storage Lab is a research group based at Huawei Canada's Toronto Research Centre that is focused on next-generation data and storage technologies and innovations. Our team comprises graduate computer engineers and computer scientists with diverse industry experience, ranging from 0 to over 20 years. This lab investigates various data storage-related topics, including data management, data catalog, data fabric, file systems, storage networks, and AI storage, aiming to advance the field and drive data storage technological progress in the new AI era.
About the job:
Conduct applied research on next-gen AI data storage and retrieval systems, including vector-native data lakes, intelligent caching, and high-performance RAG infrastructure.
Pioneer research in Agent memory systems, Knowledge Bases, and RAG-optimized intelligence, driving innovation in hybrid search, dynamic indexing, chunking/parsing, and real-time context freshness at petabyte scale.
Design and implement data, metadata, and pipeline components with robust lineage, versioning, and lifecycle management, prototyping unified architectures for file storage, data lakes, and vector indexes.
Optimize the data plane for distributed shuffling, fast checkpointing, model/KV Cache loading/offloading, and low-latency metadata serving for billion-scale entities.
Develop system software integrating AI storage, data preparation, and orchestration frameworks (Ray, Kubernetes, Slurm), eliminating bottlenecks from curation to serving.
Optimize end-to-end data paths from persistent storage to compute nodes, leveraging NVMe, RDMA, networking, prefetching, and async I/O to meet enterprise AI demands.
Publish influential research on Agent Memory, RAG, and AI-native data management at top-tier conferences, and contribute to patents and industry forums.
Collaborate with global research and engineering teams on architecture reviews, performance optimizations, and execution of the strategic roadmap.
The total target annual compensation for this position ranges from $127,000 - $225,000 depending on education, experience, and demonstrated expertise.
About the ideal candidate:
Ph.D. or Master's in Computer Science or related field, with a strong research background in data management, information retrieval, storage systems, or AI/ML infrastructure.
Deep research expertise in RAG, Agent memory, vector databases, or indexing technologies, demonstrated through first-author publications at top-tier conferences.
Strong systems building and prototyping skills, with proficiency in C/C++ and Python, and experience with distributed storage APIs (S3, HDFS, POSIX), data processing frameworks, or vector indexing libraries (FAISS, ScaNN).
Solid understanding of data storage architectures (object stores, distributed file systems, caching layers) and retrieval challenges including hybrid search, index freshness, and cost/latency trade-offs between dense and sparse methods.
Familiarity with AI/ML development lifecycles, including data drift, distributed data loading, LLM pre-training vs. fine-tuning, and advanced RAG techniques (HyDE, Multi-Query, Contextual Compression).
Excellent research communication skills, with the ability to articulate complex ideas across hardware, software, and AI disciplines, and a strong desire to bridge academic research with production-grade systems.
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.