Senior Site Reliability Engineer, DGX Cloud

NVIDIA

. Build, implement and support operational and reliability aspects of large-scale Kubernetes clusters with focus on performance at scale, real-time monitoring, logging, and alerting .

Posted 9/15/2026full-timeRemote • California • United StatesSenior💰 $168,000 - $333,500 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expert-level knowledge in Kubernetes administration, containerization, and microservices architecture, with a strong focus on operational reliability and performance at scale. Proficient in building observability stacks and managing GPU workloads across multiple cloud platforms.

Highest-signal resume keywords

Kubernetes AdministrationInfrastructure Automation ToolsSRE PrinciplesObservability Stack DevelopmentGPU Workload Management

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

KubernetesContainerizationMicroservices ArchitecturePythonGoLinux Operating SystemsNetworking FundamentalsSLOs/SLIsObservability ToolsAI Inference Workloads

Soft Skills

Incident ManagementRoot-Cause AnalysisCollaboration

Tools & Technologies

TerraformAnsiblePrometheusGrafanaELK StackOpenTelemetryTemporalAirflowKubeVirtCUDA

Certifications & Qualifications

BS in Computer Science

Industry Keywords

Cloud Security StandardsOperational ReliabilityPerformance MonitoringCapacity ManagementBlameless Postmortems

Tech Stack

Tools & technologies

AirflowAnsibleAWSAzureChefCloudGoogle Cloud PlatformGrafanaKubernetesLinuxMicroservicesPrometheusPuppetPythonPyTorchSplunkTCP/IPTerraformGo

About the role

Key responsibilities & impact

Build, implement and support operational and reliability aspects of large-scale Kubernetes clusters with focus on performance at scale, real-time monitoring, logging, and alerting
Define SLOs/SLIs, monitor error allowances, and streamline reporting
Support services before launch through system creation consulting, developing software tools, platforms and frameworks, capacity management, and launch reviews
Maintain live services by measuring and supervising availability, latency, and overall system health
Operate and optimize GPU workloads across AWS, GCP, Azure, OCI, and private clouds
Scale systems sustainably through automation and drive 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

What you’ll need

BS in Computer Science or related technical field, or equivalent experience
8+ years of experience operating production services
Expert-level knowledge of Kubernetes administration, containerization, and microservices architecture
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
Solid grasp of SRE principles, such as SLOs, SLIs, error budgets, and incident management
Experience building and operating comprehensive observability stacks (monitoring, logging, tracing) using tools like OpenTelemetry, Prometheus, Grafana, ELK Stack, Lightstep, Splunk, etc.
Operating GPU-accelerated clusters with KubeVirt in production
Applying generative-AI techniques to reduce operational toil
Experience with workflow orchestration platforms such as Temporal, Cadence, Airflow, Argo Workflows, or Step Functions
Experience operating and resolving problems in production AI inference workloads across the model-to-GPU stack, including vLLM, SGLang, PyTorch, TensorRT-LLM, NVIDIA Dynamo, CUDA, NCCL, and GPU performance analysis

Benefits

Comp & perks

Equity
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