Senior Software Engineer, Vector Index Research

Zilliz•Redwood City, CA

About The Position

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 fundamentals, performance optimization skills, and engineering taste.

Requirements

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
Interest in using AI tools to improve research, coding, testing, benchmarking, documentation, and performance analysis

Responsibilities

Research, evaluate, and implement new vector indexing and retrieval algorithms for Milvus, Zilliz Cloud, and Vector Lakebase
Read papers and track emerging work in vector search, ANN algorithms, index structures, quantization, compression, reranking, GPU acceleration, and AI retrieval systems
Build high-performance vector indexing components, including index building, query paths, vector preprocessing, quantization, compression, memory layout, and CPU/GPU acceleration
Optimize vector retrieval performance across latency, throughput, recall, memory usage, index build time, and cost efficiency
Design benchmarks and evaluation frameworks to compare algorithms and implementations under real data scale, real query patterns, and real AI workloads
Debug and solve complex performance issues across algorithm implementation, CPU/GPU execution, SIMD/vectorization, memory access, concurrency, and I/O
Turn research prototypes into maintainable, testable, and evolvable production-grade indexing capabilities
Use AI tools across the research and engineering workflow, including paper analysis, prototype generation, code implementation, testing, benchmarking, documentation, and performance analysis