Sr. Reliability Engineer (26861)

Super Micro Computer, Inc. • San Jose, CA

55d

About The Position

As a Cloud Reliability Engineer for our Linux-based AI cloud platforms, you will help us deploy, scale, and ensure high availability, performance, scalability, and security across GPU-accelerated compute clusters, Kubernetes workloads, and supporting storage/network infrastructure. You'll bridge Dev and Ops by automating infrastructure deployment, enhancing observability, and applying SRE best practices to support reliable AI and MLOps environments.

Requirements

• Bachelor's degree in Computer Science, Engineering, or a related field-or equivalent experience and 8 years of experience in the areas below
• Proficiency in Linux (Ubuntu, RHEL/CentOS), containers (Docker, Podman), and orchestration (Kubernetes).
• Experience managing GPU compute clusters (NVIDIA / CUDA, AMD / ROCm)
• Hands-on experience with observability tools (Prometheus, Grafana, Loki, ELK, etc.).
• Strong scripting and coding skills (Bash, Python, or Go).
• Exposure to secure multi-tenant environments and zero trust architectures.
• Familiarity with network protocols, DNS, DHCP, BGP, ROCEv2, and InfiniBand or high-throughput Ethernet fabrics.
• Excellent collaboration and communication skills for cross-team, partner, and customer initiatives

Nice To Haves

• Understanding of AI/ML reference architectures and experience with workflows, MLFlow, or Kubeflow.
• Familiarity with storage backends optimized for AI (CephFS, BeeGFS, WekaFS).
• Prior experience in bare-metal provisioning via PXE, Ironic, or Foreman.
• Understanding of NVIDIA GPU telemetry and NCCL testing for performance benchmarking.
• Familiarity with ITIL processes or structured change management in production systems is a plus.
• Certifications: CKA, CKAD, Linux+, or related credentials.

Responsibilities

• Cloud Infra Automation: Design and provision cloud infrastructure using Infrastructure as Code (Terraform, Ansible, or Helm) on bare metal or cloud platforms. Develop custom automation and tooling in Python or Go to extend deployment workflows and streamline operations.
• Platform Reliability: Deploy, scale, maintain, and optimize uptime for AI cloud services including GPU clusters, Kubernetes (K8s), and storage systems (e.g., Ceph, BeeGFS, or Weka). Understand the tools required to benchmark and assure consistent application performance.
• Monitoring & Alerting: Implement observability tools (e.g., Prometheus, Grafana, ELK, Loki, Fluentd) to monitor system health and alert on anomalies or performance degradation.
• Capacity Planning: Analyze usage trends and forecast infrastructure needs to support AI workloads and large-scale model training/inference.
• Incident Management: Lead root cause analysis and resolution for system outages or degraded performance. Define and maintain service level objectives (SLOs), indicators (SLIs), and agreements (SLAs) aligned with uptime and performance goals.
• CI/CD Integration: Collaborate with DevOps and MLOps teams to ensure reliable delivery pipelines using GitLab CI/CD, ArgoCD, or similar tools.
• Security & Compliance: Harden Linux systems, manage TLS certificates, and enforce secure access controls via Role-Based Access Control (RBAC), LDAP-integrated SSO, TLS, and network segmentation policies.
• Documentation & Playbooks: Maintain clear, version-controlled documentation, including architecture diagrams, runbooks, and incident response playbooks to support cross-team knowledge transfer and rapid onboarding.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Industry: Computer and Electronic Product Manufacturing
• Number of Employees: 5,001-10,000 employees