NVIDIA
AI Computing
SeniorProductionEngineer-DGXCloud
Neural analysis suggests this role is
optimal for Senior candidates.
“Senior Production Engineer - DGX Cloud at NVIDIA. Skills: Production Engineering, DevOps, SRE principles, AI Infrastructure, large-scale production systems, Go, Python, Kubernetes. scale up its AI Infrastructure. production systems that enable large scalable GPU clusters to be used for a variety of AI workloads”
What You'll Achieve.
scale up its AI Infrastructure; enabling industry leading reliability, availability, and scalability of GPU assets; maximum performance
Industry & Context.
Evaluating system failures
What They're Looking For.
Must Have
Direct experience in a Production Engineering/DevOps/SRE role within a highly technical organization with demonstrable impact from your work, 8+ years in similar role and experience on large-scale production systems, Experience with the aforementioned Production Engineering/DevOps/SRE principles, tools and techniques, BS in Computer Science, Engineering, Physics, Mathematics or a comparable Degree or equivalent experience, Technical knowledge, including a systems programming language (Go, Python) and a solid understanding of data structures and algorithms
Nice to Have
Technical competency in managing and automating large-scale distributed systems independent of cloud providers, Advanced hands-on experience and deep understanding of cluster management systems (Kubernetes, Slurm, Bright Cluster Manager), Proven operational excellence in maintaining reliable and performant AI infrastructure
What You'll Do.
scale up its AI Infrastructure
production systems that enable large scalable GPU clusters to be used for a variety of AI workloads
working on custom software related to GPU asset provisioning
and lifecycle management across cloud providers
Implementing monitoring and health management capabilities that enable industry leading reliability
and scalability of GPU assets
harnessing multiple data streams
ranging from GPU hardware diagnostics to cluster and network telemetry
ensure production AI clusters run reliability and consistently with maximum performance
Evaluating system failures and improving services based on a well-defined incident management process
How You'll Work.
Team & Collaboration
work successfully with multi-functional teams; coordinate effectively across organizational boundaries and geographies; Working with teams across NVIDIA
Communication Scope
communication skills
Full Job Description
NVIDIA is hiring experienced Senior Production Engineers to help scale up its AI Infrastructure. We expect you to have significant experience with site reliability principles and techniques including reliability assessments, incident management processes, production system observability, monitoring and alerting, automated deployments and toil elimination. We view Production Engineering as a software engineering discipline and expect significant contributions to our codebase. We welcome out-of-the-box thinkers who can provide new ideas with strong execution bias. Expect to be constantly challenged, improving, and evolving for the better. You will help advance NVIDIA's capacity to build and deploy leading infrastructure solutions for a broad range of AI-based applications. If you're creative, passionate about Production Engineering, and love having fun, please apply today! For two decades, we have pioneered visual computing, the art and science of computer graphics. With the invention of the GPU - the engine of modern visual computing - the field has expanded to encompass video games, movie production, product design, medical diagnosis and scientific research. Today, we stand at the beginning of the next era, the AI computing era, ignited by a new computing model, GPU deep learning. **What you will be doing:** * You will be part of an DGX Cloud team responsible for production systems that enable large scalable GPU clusters to be used for a variety of AI workloads. This includes working on custom software related to GPU asset provisioning, configuration, and lifecycle management across cloud providers. * Implementing monitoring and health management capabilities that enable industry leading reliability, availability, and scalability of GPU assets. You will be harnessing multiple data streams, ranging from GPU hardware diagnostics to cluster and network telemetry. * Working with teams across NVIDIA to ensure production AI clusters run reliability and consistently with m
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