Senior Site Reliability Engineer

NVIDIA•Us, CA

About The Position

NVIDIA is seeking a Senior Site Reliability Engineer for its Production Engineering team. The team combines software and systems engineering to design, build, and maintain large-scale GPU cloud infrastructure with high efficiency and availability. The role focuses on reducing manual toil by building automation frameworks, self-healing systems, observability platforms, and infrastructure-as-code pipelines that support thousands of machines. The engineer owns the full lifecycle of their work, from architecture and development through deployment, operations, and continuous improvement. The position involves developing production-grade tools and platforms that integrate data center systems, monitoring large-scale GPU and Kubernetes environments, and improving reliability through automation and incident analysis. The role includes participation in on-call rotations and sustainable incident response practices, including blameless postmortems and systematic, root-cause-focused improvements.

Requirements

At least 6 years of experience in SRE, production engineering, or infrastructure software engineering supporting large-scale, highly available environments.
BS/BTech in Computer Science, Engineering, Physics, Mathematics, or equivalent experience.
Strong software development skills, with expertise in Python and/or Go, and the ability to design, build, and own production-quality tools and services.
Deep knowledge of Kubernetes, Linux systems administration, and/or container technologies; experience handling large-scale bare-metal and cloud infrastructure.
Demonstrated experience building data center automation tools for OS provisioning, image deployment, patching, packaging, and configuration management.
Experience with CI/CD tooling and infrastructure-as-code workflows.
Strong understanding of observability practices, including metrics, logging, distributed tracing, and alerting design.
Excellent communication skills and the ability to present technical work persuasively across multifunctional teams.

Nice To Haves

Built and delivered automation or reliability platforms at large scale, measuring impact through machines handled, incidents prevented, or hours of toil eliminated.
Approach production problems as an application developer would a build: consider architecture initially, automate fully, and improve the system before moving on.
Passion for investing in groundbreaking technology and ability to learn new systems quickly.
Demonstrated ability to debug and optimize code, reduce system-level toil, and drive measurable reliability improvements through software.
Experience with DCIM systems and asset database design and implementation.

Responsibilities

Design and build production-grade automation frameworks, workflows, and data models to reduce manual operational work across provisioning, patching, image deployment, telemetry, and change management at large scale.
Engage in the complete lifecycle of services, from deployment to incident response and long-term reliability improvement.
Develop software tools and platforms that integrate data center systems, including DCIM, alerting and observability stacks, compute, storage and networking control planes, and CI/CD pipelines.
Monitor and handle large-scale GPU and Kubernetes production environments, using observability tools such as Grafana, OpenTelemetry, and PagerDuty to detect, prevent, and resolve incidents.
Build predictive and auto-healing systems by integrating incident-analysis insights into automated break-fix and self-remediation solutions.
Participate in on-call rotations and sustainable incident response, including blameless postmortems and systematic root-cause-focused improvements.
Scale systems sustainably through code by developing automation and self-service tools that improve reliability and engineering velocity.