PhD in Computer Science, Information Retrieval, Data Management, or a related field.
3+ years of research experience in data systems, information retrieval, or AI/ML infrastructure.
Proven track record of publishing at top-tier conferences like VLDB, SIGIR, and NeurIPS.
Strong understanding of AI Data Platform challenges such as hybrid search and index freshness.
Hands-on experience with unstructured data processing and vector databases.
Exceptional communication skills for diverse audiences.
Proven ability to influence through publications and patents.
Responsibilities
Define and drive the research agenda for AI-native data platforms.
Pioneer research in Agent memory systems and Knowledge Bases.
Lead research on dynamic data indexing and hybrid search strategies.
Design and prototype next-generation AI-ready data architectures.
Publish groundbreaking research at top-tier venues and secure patents.
Represent the organization as a thought leader in the community.
Collaborate with teams to translate research into production-ready systems.
Mentor junior researchers and PhD interns, fostering a collaborative environment.
Benefits
Immediate permanent position with long-term career prospects.
Opportunity to lead pioneering research in cutting-edge AI technologies.
Access to top-tier academic and industry conferences for publishing and networking.
Involvement in shaping the future of AI-native data platforms.
Collaboration with a highly skilled team in a supportive environment.
Full Job Description
Huawei Canada has an immediate permanent opening for a Senior Principal Researcher.
About the job:
Define and drive the long-term research agenda for AI-native data platforms, focusing on vector-native data lakes, intelligent caching, and high-performance retrieval infrastructures for LLMs and RAG.
Pioneer research in Agent memory systems, Knowledge Bases, and RAG-optimized data intelligence, advancing the field of Data-Centric AI.
Lead research in dynamic data indexing, hybrid search (semantic + keyword), intelligent chunking/parsing strategies, and real-time context freshness.
Design and prototype next-generation architectures that unify file storage, data lakes, and low-latency vector indexes into a cohesive, AI-ready data stack.
Publish groundbreaking research at top-tier venues (e.g., VLDB, SIGIR, NeurIPS, SIGMOD) and secure patents for novel approaches.
Represent the organization as a thought leader through speaking engagements and active participation in the academic and industry community.
Collaborate with product and engineering teams to translate research into production-ready systems with tangible real-world impact.
Mentor junior researchers and PhD interns, fostering a collaborative, high-impact research environment.
About the ideal candidate:
PhD in Computer Science, Information Retrieval, Data Management, or a related field.
3+ years of research experience in data systems, information retrieval, or AI/ML infrastructure.
Proven track record of publishing at top-tier venues such as VLDB, SIGIR, NeurIPS, SIGMOD, or ICDE.
Strong understanding of AI Data Platform challenges: hybrid search tuning, index freshness, multi-tenancy in vector spaces, and cost/latency trade-offs.
Hands-on experience with unstructured data processing, metadata enrichment, vector databases (e.g., Milvus, Pinecone), or Data Lakes (e.g., Databricks, Snowflake).
Exceptional communication skills with the ability to articulate complex research concepts to both academic and industry audiences.
Proven ability to influence the field through publications, patents, and cross-functional collaboration across research, product, and engineering.
