NVIDIA

Senior Site Reliability Engineer

NVIDIA

full-time

Posted on:

Origin:  • 🇺🇸 United States • California

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Salary

💰 $208,000 - $333,500 per year

Job Level

Senior

Tech Stack

AnsibleAWSAzureChefCloudDistributed SystemsDNSGoGoogle Cloud PlatformGrafanaKubernetesLinuxMicroservicesPrometheusPuppetPythonSplunkTCP/IPTerraform

About the role

  • Support large-scale Kubernetes services before they launch through system creation consulting, developing software tools, platforms, and frameworks, capacity management, and launch reviews
  • Build, implement and support operational and reliability aspects of large-scale Kubernetes clusters with a focus on performance at scale, real-time monitoring, logging and alerting
  • Define SLOs/SLIs, monitor error budgets, and streamline reporting
  • Maintain services once they are live by measuring and monitoring availability, latency, and overall system health
  • Operate and optimize GPU workloads across AWS, GCP, Azure, OCI, and private clouds
  • Scale systems sustainably through mechanisms like automation and evolve systems by pushing for changes that improve reliability and velocity
  • Lead triage and root-cause analysis of high-severity incidents
  • Practice balanced incident response and blameless postmortems
  • Participate in on-call rotation to support production services

Requirements

  • BS in Computer Science or related technical field, or equivalent experience
  • 12+ years of experience operating production services at scale
  • Expert-level knowledge of Kubernetes administration, containerization, and microservices architecture, with deep experience in Kubernetes operators and distributed systems at scale
  • Experience with infrastructure automation tools (Terraform, Ansible, Chef, Puppet)
  • Proficiency in at least one high-level programming language (e.g., Python, Go)
  • In-depth knowledge of Linux operating systems, networking fundamentals (TCP/IP), and cloud security standards
  • Demonstrated ability to troubleshoot complex DNS, network, Kubernetes, and systems issues in production environments
  • Proficient knowledge of SRE principles, encompassing SLOs, SLIs, error budgets, and incident handling
  • Experience building and operating comprehensive observability stacks (monitoring, logging, tracing) using tools like OpenTelemetry, Prometheus, Grafana, ELK Stack, Lightstep, Splunk, Datadog, etc.
  • Ways to stand out from the crowd: Operating GPU-accelerated clusters with KubeVirt in production; Applying generative-AI techniques to reduce operational toil; Automating incidents with Shoreline or StackStorm