Level

NVIDIA

Senior Software Engineer, Distributed Systems Engineer - DGX Cloud

AI in this role

Senior Software Engineer focusing on distributed systems and Kubernetes infrastructure to scale AI cloud workloads.

kubernetesgpus
distributed-systemscluster-operationsgpu-schedulingmonitoring

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 technical organization with demonstrable impact from your work.  Software development experience with kubernetes APIs and frameworks not just operating a cluster.
  • Highly motivated with strong communication skills, you can work successfully with multi-functional teams, principles, and architects and coordinate effectively across organizational boundaries and geographies.
  • 5+ years in similar role and experience on large-scale production systems.  Experience with common software engineering principles, tools and techniques. 
  • You possess a 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.  

Ways to stand out from the crowd:

  • 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.

NVIDIA is widely considered to be one of the technology world’s most desirable employers. We have some of the most forward-thinking and hardworking people on the planet working for us. If you are creative and autonomous, we want to hear from you!

Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 152,000 USD - 241,500 USD for Level 3, and 184,000 USD - 287,500 USD for Level 4.

You will also be eligible for equity and benefits.

Applications for this job will be accepted at least until October 2, 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.

How we rate this

Senior Software Engineer, Distributed Systems Engineer - DGX Cloud at NVIDIA rates 85 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

  1. ●●●● Builds AI80 to 100
  2. ●●●○ Works on AI60 to 79
  3. ●●○○ Uses AI40 to 59
  4. ●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.

Prepare for this job

A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.

Skills and AI tools this role asks for

Distributed SystemsCluster OperationsGPU SchedulingMonitoringKubernetesGpus

Questions you could be asked

  1. Tell me about a project where distributed systems was part of your work. What did you do?
  2. Tell me about a project where cluster operations was part of your work. What did you do?
  3. Tell me about a project where gpu scheduling was part of your work. What did you do?
  4. Tell me about a project where monitoring was part of your work. What did you do?
  5. Walk me through how you've used Kubernetes in your day-to-day work.

Adapt your resume

  • List these exact terms on your resume: Distributed Systems, Cluster Operations, GPU Scheduling, Monitoring, and Kubernetes. An applicant tracking system matches the wording, not the idea.
  • Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
  • Lead with what you built, trained or shipped — this role is judged on the AI system itself, not the tools around it.

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