Z

Zilliz

Open-source vector database for AI workloads

Senior Software Engineer - Cloud Reliability

Site Reliability EngineeringConfirmed live in the last 24 hours
Full-timeRemote in USARemote

About the Job

Requirements

  • At least 3 years of experience building or operating production cloud infrastructure, distributed systems, database platforms, or large-scale online services.
  • Strong software engineering skills in Go, Python, or similar languages, with experience developing production tooling, automation, or infrastructure services.
  • Hands-on experience with Kubernetes, Docker, and at least one major cloud platform: AWS, GCP, or Azure.
  • Solid understanding of distributed systems, including availability, scalability, performance, fault tolerance, failure recovery, and operational tradeoffs.
  • Strong troubleshooting skills across Linux, networking, storage, and cloud infrastructure, including the ability to investigate complex production issues across system layers.
  • Experience supporting production services, participating in incident response, and diagnosing reliability or performance problems in live environments.
  • Familiarity with modern infrastructure and observability tools such as Terraform, Helm, Argo CD, Prometheus, Grafana, and CI/CD systems.
  • Ability to work independently and collaborate effectively with engineers across teams and time zones.
  • Bachelor's degree in Computer Science, Software Engineering, or a related field, or equivalent practical experience.

Responsibilities

  • Own production reliability by taking responsibility for the availability, stability, and operational health of Zilliz Cloud, identifying reliability risks, and driving improvements as infrastructure and customer workloads scale.
  • Participate in production on-call rotations, respond to critical incidents, investigate service disruptions, coordinate with engineering teams, and drive timely mitigation and recovery to minimize customer impact.
  • Troubleshoot production issues across Kubernetes, cloud infrastructure, networking, storage, and distributed database systems, investigating multiple layers of the stack to identify underlying causes.
  • Lead or contribute to post-incident investigations, document findings, and turn recurring failures into engineering improvements through code changes, tooling, operational procedures, or platform improvements.
  • Develop software and internal tooling for log analysis, alert correlation, incident investigation, runbook automation, and remediation workflows to reduce repetitive operational work and improve diagnosis, recovery, and maintenance.
  • Strengthen monitoring, logging, tracing, and alerting across Zilliz Cloud, improving visibility into latency, availability, throughput, resource utilization, and system health for proactive detection and faster troubleshooting.
  • Identify bottlenecks, capacity constraints, and reliability risks across customers, clusters, and workloads, and implement improvements that support scalable, efficient, and reliable operations.
  • Partner with Cloud Platform and database engineers to improve production readiness, deployment reliability, rollback capabilities, and recovery mechanisms so new capabilities can be operated safely in production.
  • Develop and maintain runbooks, troubleshooting guides, and operational documentation, and share production learnings across engineering teams.

Desired Qualifications

  • Experience with distributed databases, storage engines, search systems, or other data-intensive infrastructure.
  • Experience operating large-scale, highly multi-tenant environments, including infrastructure fleets with thousands of nodes, clusters, tenants, or customer deployments.
  • Experience with multi-cloud environments, service-level objectives (SLOs), capacity planning, or reliability engineering practices at scale.

About the Company

Zilliz

Open-source vector database for AI workloads

Zilliz builds AI data infrastructure centered on vector databases. Its flagship open-source Milvus stores and searches high-dimensional embeddings, while Zilliz Cloud offers a fully managed vector database service for enterprise AI workloads. Milvus and Zilliz Cloud organize data as vectors (including text, JSON, and geospatial information) and enable fast similarity search, retrieval, and analytics using a lake-native architecture that separates storage from compute. The company differentiates itself with open-source roots, an enterprise-ready cloud, and features like hybrid retrieval, filtering, and reranking, aimed at scalable, cost-effective AI data infrastructure for tasks such as semantic search and retrieval-augmented generation.

Company size
51-200
Company stage
Series B
Total funding
$113M
Headquarters
Redwood City, CA
Founded
2017

Recently Posted Jobs

Explore more opportunities at Zilliz

See more jobs