Senior Software Engineer, Database Systems

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 Database Systems team builds the core engine behind Milvus and Zilliz Cloud Vector Lakebase; spanning storage, query execution, indexing, load balancing, resource management, and distributed system architecture. We build the next generation of vector databases around three Natives:

For this team, scalability, performance, cost efficiency, and reliability are the core engineering goals.

Design and build core modules of Milvus and Vector Lakebase, including storage, query engine, indexing, load balancing, resource management, and distributed execution

Drive the evolution of the engine architecture, bringing Lake-native capabilities, elastic scaling, and large-scale multi-tenancy into production to support the next stage of growth

Design database capabilities for AI and Agent workloads, handling more complex access patterns, higher concurrency, larger scale, and lower latency

Own the stability of Zilliz Cloud, working closely with cloud platform and product engineering teams to bring engine capabilities into production and using real customer scenarios to drive engine improvements

Iterate on AI developer tooling, bringing AI into the engineering workflow to improve team velocity and system observability

Contribute to the Milvus open-source community through design docs, code, code reviews, and technical discussions

3+ years of experience building database systems, distributed systems, storage engines, query engines, search systems, or large-scale data infrastructure

Bachelor's degree in Computer Science, Software Engineering, or a related field, or equivalent practical experience

Strong systems programming ability in one or more of C++, Go, or Rust

Deep understanding of distributed systems and database internals, including consistency, scalability, performance, fault tolerance, query execution, storage, indexing, resource management, and engineering tradeoffs

Strong engineering taste: the ability to judge what design is simpler, what abstraction will last, and which performance and cost tradeoffs actually matter

Experience with large-scale cloud-native databases, storage systems, search systems, or data platforms is a strong plus; open-source contributions in databases, storage, search, Kubernetes, or distributed systems are a strong plus

Deep systems engineering: We care about correctness, performance, simplicity, and long-term architecture, not just shipping features

AI-accelerated engineering: We use AI to assist with coding, testing, documentation, and design drafts, but human engineering taste matters more: deciding what's worth building, what design is simpler, and what abstraction will last

High growth, fast pace: We move fast at this stage and value ownership, fast learning, and the drive to take on hard problems. This team suits engineers who want a steep growth curve and rapid leveling-up

Fast and pragmatic: We work on hard database problems, but we ship them into real products used by real customers