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NVIDIA

Designs the chips and software that power AI

Principal Site Reliability Engineer

Site Reliability EngineeringUpdated on Nov 7
Full-timeIn-person

About the Job

Requirements

  • 15+ years of experience in Site Reliability Engineering, Platform Engineering, Distributed Systems, Cloud Architecture, or related infrastructure engineering roles.
  • A 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.
  • 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 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.

Desired Qualifications

  • 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.

About the Company

NVIDIA

Designs the chips and software that power AI

NVIDIA designs the chips and software that power artificial intelligence and sells complete data center systems to cloud providers, AI labs, and other businesses, along with GeForce graphics cards for gamers. Its GPUs, chips that perform thousands of calculations at once, became the main hardware for AI after researchers used them to train the AlexNet image-recognition model in 2012. NVIDIA connects many GPUs with its own processors and networking gear into servers and rack-sized computers, and developers program them through its CUDA software. These systems bring in most of its sales, and it also makes chips for cars and robots. Jensen Huang co-founded NVIDIA in 1993, and it is based in Santa Clara, California.

Company size
10,001+
Company stage
IPO
Headquarters
Santa Clara, CA
Founded
1993

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