Senior Site Reliability Engineer, DGX Cloud at NVIDIA

Department: Site Reliability Engineering

Language

Setup: Remote - China
Location: Zurich, Zurich
Level: senior
Posted: Aug 20, 2026

Description

NVIDIA is hiring a Senior Site Reliability Engineer to maintain and improve large-scale Kubernetes clusters supporting DGX Cloud for AI researchers and enterprise clients. The role covers operational reliability, SLO/SLI and error-budget management, service launch and post-launch monitoring, GPU workload optimization across major cloud providers, automation, incident triage, root-cause analysis, and on-call support. Candidates need a BS in Computer Science or related technical field or equivalent experience, at least 10 years of production-service operations experience, and expertise in Kubernetes, containerization, microservices, infrastructure automation, programming, Linux, networking, cloud security, observability, and SRE practices.

Required Skills

Kubernetes administration
Containerization
Microservices architecture
Terraform
Python
Linux
TCP/IP
Cloud security
SRE principles
OpenTelemetry
Prometheus
Grafana
GPU performance analysis

Additional Information

Minimum Education: Bachelor of Science in Computer Science or related technical field, or equivalent experience
Required Previous Experiences: 10+ years of experience operating production services
Expert-level knowledge of Kubernetes administration, containerization, and microservices architecture
Experience with infrastructure automation tools such as Terraform, Ansible, Chef, or Puppet
Proficiency in at least one high-level programming language such as Python or Go
In-depth knowledge of Linux operating systems, TCP/IP networking, and cloud security standards
Proficient knowledge of SRE principles, including SLOs, SLIs, error budgets, and incident handling
Experience building and operating comprehensive observability stacks using tools such as OpenTelemetry, Prometheus, Grafana, ELK Stack, Lightstep, or Splunk
Preferred Qualifications: 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 troubleshooting production AI inference workloads across the model-to-GPU stack, including vLLM, SGLang, PyTorch, TensorRT-LLM, NVIDIA Dynamo, CUDA, NCCL, and GPU performance analysis