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. We're entering our next phase of 10x growth: more customers, larger datasets, more complex AI workloads, and higher expectations for reliability, performance, and cost efficiency. You'll join a small, fast-moving Cloud Platform team building the core platform capabilities that run Zilliz Cloud at scale across multi-cloud environments. This is not a standard control-plane platform role. You will work across both the cloud platform and database runtime, building cloud-and-engine integrated systems around Vector Lakebase so AI workloads can be scheduled faster, run more reliably, and serve real customers more efficiently. What you'll do: Design and build the cloud platform behind Zilliz Cloud and Vector Lakebase, bringing together cloud control plane, database runtime, scheduling, resource management, deployment, and lifecycle management to support fast workload placement, elastic scaling, multi-tenant isolation, and cost-efficient execution Build cloud-native systems that make distributed database provisioning, scaling, upgrades, recovery, and workload migration automated, observable, rollback-safe, and efficient Work deep across Kubernetes, multi-cloud infrastructure, networking, storage, and database engine runtimes to deliver a tightly integrated cloud-and-engine product experience Improve platform scalability, reliability, performance, and operational simplicity as we grow across customers, regions, tenants, datasets, and AI workloads Partner with database, reliability, and product engineers to bring new Vector Lakebase capabilities into cloud production safely and quickly Use AI deeply across the platform engineering workflow, including deployment validation, diagnosis, incident analysis, capacity planning, documentation, code generation, and operational tooling What we're looking for: 3+ years of experience building production systems such as large-scale SaaS platforms, data platforms, AI applications, microservices, or cloud infrastructure Bachelor's degree in Computer Science, Software Engineering, or a related field, or equivalent practical experience Strong hands-on experience with Kubernetes, Docker, and at least one major cloud platform such as AWS, GCP, or Azure Familiarity with infrastructure automation and cloud operations tooling such as Terraform, Helm, Argo CD, Prometheus, Grafana, CI/CD systems, or similar tools Experience building cloud-native platform systems is a strong plus, including scheduling, orchestration, deployment, configuration, upgrades, lifecycle management, or resource management Understanding of distributed databases or database engine internals is a strong plus, especially around scalability, performance, reliability, and multi-tenant isolation Strong interest in AI-assisted development and engineering productivity. We value engineers who actively use AI to multiply their output across coding, debugging, testing, documentation, and operations How we operate: High ownership: You own platform outcomes end-to-end, from design to production behavior, not just a narrow slice of the system AI-first engineering: We actively use AI to improve coding, testing, documentation, diagnosis, and operations, but human engineering taste still matters most Fast and focused: We ship often while keeping a high bar. This team suits engineers who want speed, autonomy, and a steep growth curve Global collaboration: We work closely with engineering teams across APAC and the US, designing collaboration around timezone coverage to support customers globally 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

Build cloud-native systems that make distributed database provisioning, scaling, upgrades, recovery, and workload migration automated, observable, rollback-safe, and efficient
Work deep across Kubernetes, multi-cloud infrastructure, networking, storage, and database engine runtimes to deliver a tightly integrated cloud-and-engine product experience
Improve platform scalability, reliability, performance, and operational simplicity as we grow across customers, regions, tenants, datasets, and AI workloads
Partner with database, reliability, and product engineers to bring new Vector Lakebase capabilities into cloud production safely and quickly
Use AI deeply across the platform engineering workflow, including deployment validation, diagnosis, incident analysis, capacity planning, documentation, code generation, and operational tooling
3+ years of experience building production systems such as large-scale SaaS platforms, data platforms, AI applications, microservices, or cloud infrastructure
Bachelor's degree in Computer Science, Software Engineering, or a related field, or equivalent practical experience
Strong hands-on experience with Kubernetes, Docker, and at least one major cloud platform such as AWS, GCP, or Azure
Familiarity with infrastructure automation and cloud operations tooling such as Terraform, Helm, Argo CD, Prometheus, Grafana, CI/CD systems, or similar tools
Experience building cloud-native platform systems is a strong plus, including scheduling, orchestration, deployment, configuration, upgrades, lifecycle management, or resource management
Understanding of distributed databases or database engine internals is a strong plus, especially around scalability, performance, reliability, and multi-tenant isolation
Strong interest in AI-assisted development and engineering productivity. We value engineers who actively use AI to multiply their output across coding, debugging, testing, documentation, and operations
High ownership: You own platform outcomes end-to-end, from design to production behavior, not just a narrow slice of the system
AI-first engineering: We actively use AI to improve coding, testing, documentation, diagnosis, and operations, but human engineering taste still matters most
Fast and focused: We ship often while keeping a high bar. This team suits engineers who want speed, autonomy, and a steep growth curve
Global collaboration: We work closely with engineering teams across APAC and the US, designing collaboration around timezone coverage to support customers globally

Nice-to-haves

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Application prep

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Keywords to mirror

aikubernetesdockerawsgcpazureci/cd

Platform: lever
Effort: quick

Benefits

Benefits detected in the listing.

Dental

Vision

401(K)

Paid Time Off