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 Vector Index team focuses on building the core vector retrieval capabilities behind Milvus, Zilliz Cloud, and Vector Lakebase. We work on making similarity search over massive embedding datasets faster, more accurate, and more cost-efficient, while continuously advancing ANN algorithms, index structures, quantization, compression, recall optimization, CPU/GPU acceleration, and high-performance retrieval frameworks.
This role sits at the intersection of research and engineering. You will read papers, evaluate new algorithms, build prototypes, and turn promising ideas into production-grade vector indexing and retrieval systems. We are looking for engineers who enjoy research, but also have strong engineering fundamentals, performance optimization skills, and engineering taste.
What you'll do:Research, evaluate, and implement new vector indexing and retrieval algorithms for Milvus, Zilliz Cloud, and Vector LakebaseRead papers and track emerging work in vector search, ANN algorithms, index structures, quantization, compression, reranking, GPU acceleration, and AI retrieval systemsBuild high-performance vector indexing components, including index building, query paths, vector preprocessing, quantization, compression, memory layout, and CPU/GPU accelerationOptimize vector retrieval performance across latency, throughput, recall, memory usage, index build time, and cost efficiencyDesign benchmarks and evaluation frameworks to compare algorithms and implementations under real data scale, real query patterns, and real AI workloadsDebug and solve complex performance issues across algorithm implementation, CPU/GPU execution, SIMD/vectorization, memory access, concurrency, and I/OTurn research prototypes into maintainable, testable, and evolvable production-grade indexing capabilitiesUse AI tools across the research and engineering workflow, including paper analysis, prototype generation, code implementation, testing, benchmarking, documentation, and performance analysisWhat we're looking for:3+ years of experience in vector search, ANN algorithms, search systems, high-performance computing, or performance-critical systemsBachelor's degree in Computer Science, Software Engineering, or a related field, or equivalent practical experienceStrong C++ or Rust programming ability and solid engineering fundamentalsExperience with vector similarity search, ANN algorithms, index structures, quantization, compression, reranking, or high-performance retrieval systems is a strong plusStrong interest in research-driven engineering: reading papers, evaluating tradeoffs, building prototypes, and turning ideas into production systemsExperience with performance optimization and systematic debugging is a strong plus, especially around CPU/GPU execution, SIMD, memory layout, concurrency, I/O, or large-scale data processingInterest in using AI tools to improve research, coding, testing, benchmarking, documentation, and performance analysisHow we operate:Research-driven, production-focused: We track frontier algorithms, but care most about whether they work under real data scale, real query patterns, and real production constraintsExtreme performance: We care about every memory access, every query path, and every tradeoff between recall and latencyAI-first engineering: We actively use AI to accelerate paper reading, prototyping, coding, testing, documentation, and performance analysis, but human judgment and engineering taste still matter mostFast and pragmatic: We work on hard vector indexing and retrieval problems, but we ship them into Milvus, Zilliz Cloud, and Vector LakebaseOpen source by default: Milvus is a core part of our engineering culture, and strong indexing capabilities should stand up to public design, code, and community usageBenefits:Competitive compensation (cash + equity)Regular bonus and equity refresh opportunitiesMedical, dental, and vision insurancePaid time off, including vacation, sick leave, and global reset/wellbeing daysGenerous 401(k) and regional retirement plansZilliz is an Equal Opportunity Employer and welcomes people from all backgrounds, experiences, abilities, and perspectives. All qualified applicants will receive consideration for employment regardless of race, color, national origin, religion, sexual orientation, gender, gender identity, age, physical disability, or length of time spent unemployed.
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
Read LessZilliz 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 executionBuild cloud-native systems that make distributed database provisioning, scaling, upgrades, recovery, and workload migration automated, observable, rollback-safe, and efficientWork deep across Kubernetes, multi-cloud infrastructure, networking, storage, and database engine runtimes to deliver a tightly integrated cloud-and-engine product experienceImprove platform scalability, reliability, performance, and operational simplicity as we grow across customers, regions, tenants, datasets, and AI workloadsPartner with database, reliability, and product engineers to bring new Vector Lakebase capabilities into cloud production safely and quicklyUse AI deeply across the platform engineering workflow, including deployment validation, diagnosis, incident analysis, capacity planning, documentation, code generation, and operational toolingWhat we're looking for:3+ years of experience building production systems such as large-scale SaaS platforms, data platforms, AI applications, microservices, or cloud infrastructureBachelor's degree in Computer Science, Software Engineering, or a related field, or equivalent practical experienceStrong hands-on experience with Kubernetes, Docker, and at least one major cloud platform such as AWS, GCP, or AzureFamiliarity with infrastructure automation and cloud operations tooling such as Terraform, Helm, Argo CD, Prometheus, Grafana, CI/CD systems, or similar toolsExperience building cloud-native platform systems is a strong plus, including scheduling, orchestration, deployment, configuration, upgrades, lifecycle management, or resource managementUnderstanding of distributed databases or database engine internals is a strong plus, especially around scalability, performance, reliability, and multi-tenant isolationStrong 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 operationsHow 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 opportunitiesMedical, dental, and vision insurancePaid time off, including vacation, sick leave, and global reset/wellbeing daysGenerous 401(k) and regional retirement plansZilliz is an Equal Opportunity Employer and welcomes people from all backgrounds, experiences, abilities, and perspectives. All qualified applicants will receive consideration for employment regardless of race, color, national origin, religion, sexual orientation, gender, gender identity, age, physical disability, or length of time spent unemployed.
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
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