Job description

NVIDIA is driving AI and high-performance computing forward. DGX Cloud aims to deliver a fully managed AI platform on major cloud providers, optimizing AI workloads using high-performance NVIDIA infrastructure. Work with NVIDIA's DGX Cloud team as a Senior Site Reliability Engineer to maintain high-performance DGX Cloud clusters for AI researchers and enterprise clients worldwide. What makes this opportunity outstanding is that you will be at the forefront of technology, working with innovative AI and cloud computing solutions. You will have the chance to contribute to a world-class team that is determined to push the boundaries of innovation and flawlessly implement ambitious projects! What you’ll be doing: • 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 they launch through system creation consulting, developing software tools, platforms and frameworks, capacity management, and launch reviews. • Maintain services once they are live 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 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. • 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. Ways to stand out from the crowd: • 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. Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 168,000 USD - 270,250 USD for Level 4, and 208,000 USD - 333,500 USD for Level 5. You will also be eligible for equity and benefits . Applications for this job will be accepted at least until September 19, 2026. This posting is for an existing vacancy. NVIDIA uses AI tools in its recruiting processes. NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.

Requirements

Must-haves

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 they launch through system creation consulting, developing software tools, platforms and frameworks, capacity management, and launch reviews.
Maintain services once they are live 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 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.
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.
You will also be eligible for equity and benefits .
Applications for this job will be accepted at least until September 19, 2026.
This posting is for an existing vacancy.

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aikubernetesawsgcpazurepythongollm

Platform: eightfold
Effort: medium