Senior Software Engineer, Database Systems

🏢 Zilliz · 📍 Redwood City · 💼 hybrid · ⏰ Full-Time 💰 $175,000 - $250,000 / yr
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Position Summary & Responsibilities

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:
  1. AI-Native: Born for AI and Agent workloads, with native support for vector data and semantic retrieval
  2. Cloud-Native: Designed for elastic scaling and large-scale operation in the cloud
  3. 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.
🎯 Candidate Preparation Guide For Zilliz Applicants

Key Role Competencies & Evaluation Highlights

Key Role Focus:

The panel rigorously tests deep systems-level understanding of vector databases, distributed systems architecture, and high-performance computing.

📋 Preparation Priorities (Key Areas to Clarify):
  • 1. Treating vector databases merely as wrappers around standard SQL databases without understanding embedding spaces
  • 2. Failing systems-design questions regarding latency trade-offs at billions-of-vectors scale
  • 3. Lack of low-level concurrency and memory optimization knowledge
✨ Recommended Role Alignment Hook:

I specialize in engineering ultra-low latency vector infrastructure to unlock real-time AI retrieval at planetary scale.

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