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:
- AI-Native: Born for AI and Agent workloads, with native support for vector data and semantic retrieval
- Cloud-Native: Designed for elastic scaling and large-scale operation in the cloud
- Lake-Native: Built on a data-lake architecture that works naturally with data at massive scale
For this team, scalability, performance, cost efficiency, and reliability are the core engineering goals.
What you will do:- 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
What we are looking for:- 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
How we operate:- 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
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
$175,000 - $250,000 a year
