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Open nowPosted 537 days ago

Senior Software Engineer, Database Systems

Zilliz12 open roles

Pay
$175,000 – $250,000 a year
Where
Redwood City, CA
Work mode
Hybrid
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Your applicationOpen nowSenior Software Engineer, Database SystemsZilliz · Redwood City, CA

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The clock on this job

Early applications get read.

13.3% of postings close within 7 days. Measured by our own scanner across the market. Zilliz postings stay open a median of 60 days.

Share of postings closed within
  1. 2.1%1 day
  2. 4.6%3 days
  3. 13.3%7 days
  4. 21.3%14 days
  5. 35.7%30 days
  6. 59.1%60 days
This job: posted 537 days ago

Zilliz median: 60 days open

The posting

About Zilliz

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:

  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.

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

Benefits

  • Comprehensive Health Coverage: Medical, dental, and vision insurance plans for employees and eligible family members.
  • Unlimited PTO: Flexible paid time off to rest, recharge, and take care of personal needs.
  • Competitive Compensation and Equity: Competitive cash compensation and long-term incentive opportunities, including stock options.
  • 401(k) with Company Match: Retirement savings plan with employer matching contributions.

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.

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