Lead Recruiter, US & Europe

🏢 Zilliz · 📍 Redwood City · 💼 hybrid · ⏰ Full-Time

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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.

About the Role

We are looking for a Lead Recruiter to own recruiting across the United States and Europe and help build the teams behind Zilliz's next stage of growth.

This is a senior individual contributor role with broad ownership across both recruiting strategy and execution. You will support technical and commercial functions, including Engineering, Product, GTM, and other business teams.

You will work closely with company and functional leaders to understand business priorities, shape hiring strategies, and deliver against them. We are looking for someone who can think strategically about where and how we hire while remaining highly hands-on in the market.

This role is based in our Redwood City office and is expected to work onsite at least three days per week.

🎯 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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