Site Reliability Engineering (SRE) at NVIDIA is an engineering discipline to design, build and maintain large scale production systems with high efficiency and availability using the combination of software and systems engineering practices. This is a highly specialized discipline which demands knowledge across different systems, networking, coding, database, capacity management, continuous delivery and deployment and open source cloud enabling technologies like Kubernetes and OpenStack. SRE at NVIDIA ensures that our internal and external facing GPU cloud services run maximum reliability and uptime as promised to the users and at the same time enabling developers to make changes to the existing system through careful preparation and planning while keeping an eye on capacity, latency and performance. SRE is also a mindset and a set of engineering approaches to running better production systems and optimizations. Much of our software development focuses on eliminating manual work through automation, performance tuning and growing efficiency of production systems.
As SREs are responsible for the big picture of how our systems relate to each other, we use a breadth of tools and approaches to tackle a broad spectrum of problems. Practices such as limiting time spent on reactive operational work, blameless postmortems and proactive identification of potential outages factor into iterative improvement that is key to both product quality and interesting dynamic day-to-day work. SRE's culture of diversity, intellectual curiosity, problem solving and openness is important to our success. Our organization brings together people with a wide variety of backgrounds, experiences and perspectives. We encourage them to collaborate, think big and take risks in a blame-free environment. We promote self-direction to work on meaningful projects, while we also strive to build an environment that provides the support and mentorship needed to learn and grow.
What you'll be doing:
Design, implement and support operational and reliability aspects of large scale Observability & Telemetry collection platform with a focus on performance at scale, real time monitoring, logging and alerting
Engage in and improve the whole lifecycle of services—from inception and design through deployment, operation and refinement
Support services before they go live through activities such as system design consulting, developing software tools, platforms and frameworks, capacity management and launch reviews
Maintain services once they are live by measuring and monitoring availability, latency and overall system health
Scale systems sustainably through mechanisms like automation, and evolve systems by pushing for changes that improve reliability and velocity
Practice sustainable incident response and blameless postmortems
Be part of an on call rotation to support production systems
What we need to see:
BS degree in Computer Science or a related technical field involving coding (e.g., physics or mathematics), or equivalent experience
5+ years of experience with Infrastructure automation, distributed systems design, experience with design, develop tools for running large scale private or public cloud system in Production
8+ years experience delivering foundational infrastructure and observability platforms.
Experience in one or more of the following: Python, Go, Perl or Ruby
In depth knowledge on Linux, Networking and Containers
Ways to stand out from the crowd:
Interest in crafting, analyzing and fixing large-scale distributed systems
Systematic problem-solving approach, coupled with strong communication skills and a sense of ownership and drive. Ability to debug and optimize code and automate routine tasks
Experience in using or running large private and public cloud systems based on Kubernetes, OpenStack and Docker. Experience running Grafana, OpenTelemetry, Prometheus, and similar observability focused tools
You will also be eligible for equity and benefits.
Top Skills
What We Do
NVIDIA’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing — with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world. Today, NVIDIA is increasingly known as “the AI computing company.”
