Nvidia Senior Site Reliability Engineer DGX Cloud 

United States, California 

123189682

What you’ll be doing:

• 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

What we need to see:

• 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 (e.g., 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

• GPU workload orchestration and large-scale GPU resource management