Senior Researcher - AI Data Platforms

Huawei Canada; Markham; On-site; CAD 127,000 - 225,000; Full time

Job summary

Huawei Canada in Toronto Research Centre is seeking a Senior Researcher to advance AI data storage and retrieval technologies at scale.

You will lead research on vector-native data lakes, RAG, and agent memory systems, collaborating with global teams to publish results and influence future IT infrastructure.

The role offers an opportunity to impact enterprise AI demands, with exposure to state-of-the-art systems and a path toward patents.

Qualifications

• Ph.D. or Master's in Computer Science with strong research background in data management, storage systems, or AI/ML infra.
• Deep expertise in RAG, Agent memory, vector databases, or indexing tech demonstrated by top-tier publications.
• Strong systems building and prototyping skills; proficient in C/C++ and Python; 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.
• 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.

Responsibilities

• 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 hybrid search, dynamic indexing, 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.

Skills

C/C++

Python

Vector databases

FAISS

ScaNN

Distributed storage

Ray

Education

PhD or MS in CS

Tools

S3

HDFS

POSIX

Kubernetes

Job description

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.

Job requirements

• 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.