Job description

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 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 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.

Requirements

Must-haves

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

Nice-to-haves

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Keywords to mirror

aigorustkubernetes

Platform: lever
Effort: quick

Benefits

Benefits detected in the listing.

Dental

Vision

401(K)

Paid Time Off