NVIDIA
AI Computing
PrincipalSoftwareEngineer,DistributedSystemsEngineer-DGXCloud
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“Principal Software Engineer, Distributed Systems Engineer - DGX Cloud at NVIDIA. Skills: Distributed Systems Engineering, AI Infrastructure, Kubernetes, GPU Resource Scheduling. 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; advance NVIDIA's capacity to build and deploy leading infrastructure solutions; enable industry leading reliability, availability, and scalability of GPU assets; ensure production AI clusters run reliability and consistently with maximum performance
Industry & Context.
Evaluating system failures
What They're Looking For.
Must Have
software engineering experience with kubernetes, cluster operations, operator development, node health monitoring, working with GPU resource scheduling, software engineering role within a highly technical organization, Software development experience with kubernetes APIs and frameworks, 15+ years in similar role, experience on large-scale production systems, common software engineering principles, tools and techniques, BS in Computer Science, Engineering, Physics, Mathematics or a comparable Degree or equivalent experience, systems programming language (Go, Python), solid understanding of data structures and algorithms
Nice to Have
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 scheduling GPU resources on kubernetes
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
Working with teams across NVIDIA to 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 software engineers with kubernetes experience to help scale up its AI Infrastructure. We expect you to have significant software engineering experience with kubernetes including cluster operations, operator development, node health monitoring and working with GPU resource scheduling. 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 kubernetes and GPUs, 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 scheduling GPU resources on kubernetes. * 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 maximum performance. Evaluating system failures and improving services based on a well-defined incident management process. **What we need to see:** * Direct experience in a software engineering role within a highly te
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