The posting
About Zilliz
Zilliz is building the next generation of data infrastructure for AI. Founded in 2017 by the creators of Milvus, one of the world's most popular open-source vector databases, we help developers and enterprises build, deploy, and scale AI-powered applications, from semantic search and RAG to emerging agentic AI workloads.
Backed by $113 million in funding from leading global investors, Zilliz has established a strong presence in the AI infrastructure market. Milvus has earned over 46,000 GitHub stars, and our technology has reached more than 10,000 enterprise users worldwide. Companies including OpenEvidence, Exa, and Filevine rely on Zilliz Cloud to power their AI applications.
Building on our roots in vector database technology, we're expanding Zilliz Cloud into a broader AI data platform with Zilliz Vector Lakebase. As AI continues to reshape how applications are built and how data is used, we're focused on making the underlying infrastructure more powerful, scalable, and accessible to organizations worldwide.
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.
What you'll do:
- 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
What we'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
- Interest in using AI tools to improve research, coding, testing, benchmarking, documentation, and performance analysis
How we operate:
- Research-driven, production-focused: We track frontier algorithms, but care most about whether they work under real data scale, real query patterns, and real production constraints
- Extreme performance: We care about every memory access, every query path, and every tradeoff between recall and latency
- AI-first engineering: We actively use AI to accelerate paper reading, prototyping, coding, testing, documentation, and performance analysis, but human judgment and engineering taste still matter most
- Fast and pragmatic: We work on hard vector indexing and retrieval problems, but we ship them into Milvus, Zilliz Cloud, and Vector Lakebase
- Open source by default: Milvus is a core part of our engineering culture, and strong indexing capabilities should stand up to public design, code, and community usage
Benefits:
- Competitive compensation (cash + equity)
- Regular bonus and equity refresh opportunities
- Medical, dental, and vision insurance
- Paid time off, including vacation, sick leave, and global reset/wellbeing days
- Generous 401(k) and regional retirement plans
Benefits
- Comprehensive Health Coverage: Medical, dental, and vision insurance plans for employees and eligible family members.
- Unlimited PTO: Flexible paid time off to rest, recharge, and take care of personal needs.
- Competitive Compensation and Equity: Competitive cash compensation and long-term incentive opportunities, including stock options.
- 401(k) with Company Match: Retirement savings plan with employer matching contributions.
Zilliz is an Equal Opportunity Employer. We consider all qualified applicants for employment without regard to race, color, religion, sex, sexual orientation, gender identity or expression, national origin, age, disability, genetic information, veteran status, or any other characteristic protected by applicable law.
