Job description

Site Reliability Engineering (SRE) at NVIDIA is an engineering discipline focused on designing, building, and operating large-scale production systems with exceptional efficiency, resilience, and availability. It combines software and systems engineering practices with deep expertise across distributed systems, networking, cloud infrastructure, Kubernetes, databases, capacity management, continuous delivery, and observability. SRE at NVIDIA ensures that our internal and external services achieve their reliability and performance objectives while enabling developers to deliver changes safely and rapidly. Our engineers automate repetitive operational work, improve system performance, build scalable platforms, and continuously strengthen the reliability of production environments. As a Principal SRE, you will shape the technical direction of NVIDIA’s AI Platform Runtime and lead reliability engineering initiatives spanning multiple organizations. You will solve complex, ambiguous problems at enterprise scale, 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 transform platform operations and developer productivity. Our SRE culture values intellectual curiosity, diversity of thought, collaboration, and learning through blameless analysis. We encourage engineers to think boldly, take thoughtful risks, and lead meaningful improvements across organizational boundaries. What you’ll be doing: 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 that power NVIDIA’s next-generation AI-driven products and services. Lead the design and development of AI agents, AI skills, and intelligent automation that accelerate 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 that improve availability, scalability, performance, security, and developer productivity. Advance observability across complex distributed environments through 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 to make high-impact architectural and investment decisions. Provide technical leadership during critical incidents and ensure that lessons from incidents translate into durable engineering and organizational improvements. Develop reference architectures, shared platform capabilities, and automation frameworks that can be adopted across multiple engineering organizations. Influence, mentor, and raise the technical bar for senior engineers and technical leaders throughout the company. What we need to see: 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, with 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. A track record of delivering measurable improvements in availability, performance, operational efficiency, or engineering productivity within complex environments. Outstanding technical judgment, communication, and collaboration skills, with the ability to influence senior stakeholders across technical and organizational boundaries. Ways to stand out from the crowd: 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. NVIDIA is leading groundbreaking developments in Artificial Intelligence, High-Performance Computing, and Visualization. The GPU, our invention, serves as the visual cortex of modern computers and is at the heart of our products and services. Our work creates new possibilities for exploration, creativity, and discovery, powering innovations that once belonged to science fiction—from artificial intelligence to autonomous vehicles. NVIDIA is looking for exceptional technical leaders like you to help architect the reliable platforms that will accelerate the next wave of artificial intelligence. #LI-Hybrid Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 248,000 USD - 396,750 USD. You will also be eligible for equity and benefits . Applications for this job will be accepted at least until September 12, 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

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 that power NVIDIA’s next-generation AI-driven products and services.
Lead the design and development of AI agents, AI skills, and intelligent automation that accelerate 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 that improve availability, scalability, performance, security, and developer productivity.
Advance observability across complex distributed environments through 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 to make high-impact architectural and investment decisions.
Provide technical leadership during critical incidents and ensure that lessons from incidents translate into durable engineering and organizational improvements.
Develop reference architectures, shared platform capabilities, and automation frameworks that can be adopted across multiple engineering organizations.
Influence, mentor, and raise the technical bar for senior engineers and technical leaders throughout the company.
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, with 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.
A track record of delivering measurable improvements in availability, performance, operational efficiency, or engineering productivity within complex environments.
Outstanding technical judgment, communication, and collaboration skills, with the ability to influence senior stakeholders across technical and organizational boundaries.
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.

Nice-to-haves

None flagged.

Application prep

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

Keywords to mirror

kubernetesaimlawsazuregcppythongotypescriptjavascriptjavamachine learning

Platform: workday
Effort: long

Benefits

Benefits detected in the listing.

Vision