Principal Site Reliability Engineer

NVIDIA•Santa Clara, CA

About The Position

NVIDIA’s Site Reliability Engineering organization designs, builds, and operates large-scale production systems with a focus on efficiency, resilience, and availability. It combines software and systems engineering with expertise in distributed systems, networking, cloud infrastructure, Kubernetes, databases, capacity management, continuous delivery, and observability. The organization helps internal and external services meet reliability and performance objectives while enabling safe, rapid software delivery. As a Principal Site Reliability Engineer, you will shape the technical direction of NVIDIA’s AI Platform Runtime and lead reliability engineering initiatives across multiple organizations. You will address complex, ambiguous, enterprise-scale problems, establish architectural standards, and influence how teams design and operate critical AI-powered services. You will also advance the use of AI agents and skills to improve platform operations and developer productivity. NVIDIA’s SRE culture emphasizes curiosity, diverse perspectives, collaboration, and learning through blameless analysis.

Requirements

15+ years of experience in Site Reliability Engineering, Platform Engineering, Distributed Systems, Cloud Architecture, or related infrastructure engineering roles.
BS or MS degree in Computer Science or a related technical field involving significant software development, or equivalent experience.
Demonstrated experience setting technical strategy and leading large-scale engineering initiatives across multiple teams or organizations.
Deep expertise in distributed systems architecture, networking, Linux, Kubernetes, and public cloud platforms such as AWS, Azure, or GCP.
Strong proficiency in one or more programming languages such as Python, Go, TypeScript, JavaScript, or Java, and experience building production-grade automation and platform software.
Extensive experience with infrastructure-as-code and platform automation technologies such as Terraform, Crossplane, AWS CDK, AWS CloudFormation, or equivalent tools.
Deep understanding of observability at scale, including OpenTelemetry and modern metrics, logging, tracing, profiling, and analytics platforms.
Expertise in reliability engineering practices, including service-level objectives, error budgets, capacity planning, fault tolerance, disaster recovery, incident management, and blameless postmortems.
Track record of delivering measurable improvements in availability, performance, operational efficiency, or engineering productivity in complex environments.
Outstanding technical judgment, communication, and collaboration skills, including the ability to influence senior stakeholders across technical and organizational boundaries.

Nice To Haves

Experience designing or operating large-scale AI/ML platforms, GPU infrastructure, inference systems, training environments, or high-performance computing platforms.
Hands-on experience building AI agents, agentic workflows, or intelligent automation for infrastructure and production operations.
Demonstrated success establishing reliability architecture and engineering standards adopted across a large enterprise.
Experience applying machine learning, analytics, or advanced automation to capacity management, anomaly detection, incident response, or autonomous remediation.
Recognized technical leadership through patents, publications, open-source contributions, industry standards, conference presentations, or widely adopted internal platforms.

Responsibilities

Define and drive the long-term technical vision, architecture, and roadmap for reliability across NVIDIA’s AI Platform Runtime and related enterprise systems.
Architect highly available, resilient, secure, and scalable distributed platforms for NVIDIA’s AI-driven products and services.
Lead the design and development of AI agents, AI skills, and intelligent automation for platform operations, incident response, troubleshooting, and remediation.
Establish platform-wide reliability standards, including service-level objectives, error budgets, capacity models, resilience patterns, and operational readiness requirements.
Identify systemic risks and lead cross-functional programs to improve availability, scalability, performance, security, and developer productivity.
Advance observability across distributed environments using OpenTelemetry, metrics, logs, traces, profiling, analytics, and automated anomaly detection.
Partner with senior leaders and engineers across Cloud, Platform, Security, Networking, and AI/ML organizations on architectural and investment decisions.
Provide technical leadership during critical incidents and ensure incident lessons produce durable engineering and organizational improvements.
Develop reference architectures, shared platform capabilities, and automation frameworks for adoption across engineering organizations.
Influence and mentor senior engineers and technical leaders throughout the company.