Senior Site Reliability Engineer, DGX Cloud

NVIDIA • Us, CA

1d

About The Position

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!

Requirements

• 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.

Nice To Haves

• 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.

Responsibilities

• 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.

Benefits

• equity
• benefits

What This Job Offers

• Job Type: Full-time
• Career Level: Senior