Zilliz offers a fully managed Vector Lakebase powered by Milvus, unifying real-time vector search, lake-scale discovery, and AI data operations. Available across AWS, Google Cloud, and Azure regions worldwide. Backed by AI Grant.
About the role
This role sits at the intersection of research and engineering. You will read papers, evaluate new algorithms, build prototypes, and turn promising ideas into production-grade vector indexing and retrieval systems. We are looking for engineers who enjoy research, but also have strong engineering fundamentals, performance optimization skills, and engineering taste.
What they're looking for
- 3+ years of experience in vector search, ANN algorithms, search systems, high-performance computing, or performance-critical systems
- Bachelor's degree in Computer Science, Software Engineering, or a related field, or equivalent practical experience
- Strong C++ or Rust programming ability and solid engineering fundamentals
- Experience with vector similarity search, ANN algorithms, index structures, quantization, compression, reranking, or high-performance retrieval systems is a strong plus
- Strong interest in research-driven engineering: reading papers, evaluating tradeoffs, building prototypes, and turning ideas into production systems
- Experience with performance optimization and systematic debugging is a strong plus, especially around CPU/GPU execution, SIMD, memory layout, concurrency, I/O, or large-scale data processing
More about this role
Zilliz is a fast-growing startup developing the industry’s leading vector database for enterprise-grade AI. Founded by the engineers behind Milvus, the world’s most popular open-source vector database, the company builds next-generation database technologies to help organizations quickly create AI applications. On a mission to democratize AI, Zilliz is committed to simplifying data management for AI applications and making vector databases accessible to every organization.
The Vector Index team focuses on building the core vector retrieval capabilities behind Milvus, Zilliz Cloud, and Vector Lakebase. We work on making similarity search over massive embedding datasets faster, more accurate, and more cost-efficient, while continuously advancing ANN algorithms, index structures, quantization, compression, recall optimization, CPU/GPU acceleration, and high-performance retrieval frameworks.
This role sits at the intersection of research and engineering. You will read papers, evaluate new algorithms, build prototypes, and turn promising ideas into production-grade vector indexing and retrieval systems. We are looking for engineers who enjoy research, but also have strong engineering...
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