Responsibilities

• Stand up and validate complete DSX-aligned software stacks on multi-node GPU systems. Capture the dependencies, configuration order, validation steps, and operational handoffs as you go.
• Turn working deployments into useful technical content: reference architectures, quick-starts, installation and upgrade guides, troubleshooting runbooks, code examples, blogs, whitepapers, and demo videos.
• Build reusable examples and automation with APIs, Python or shell scripting, infrastructure-as-code, containers, Kubernetes, Slurm, Helm, GitOps or equivalent experience, and CI/CD where they fit.
• Build demos, labs, and training that address the practical aspects of operating an AI factory, from initial deployment and tenant setup to upgrades, monitoring, scheduling, fault isolation, remediation, capacity management, and security.
• Show how the layers of the stack fit together. Work with TME, Product, Engineering, and Marketing to demonstrate how data center hardware, infrastructure and cluster management software, orchestration, AI platforms, and the workloads on top operate as one system.
• Test pre-release software using representative training and inference workloads. Identify rough edges, assess interoperability and resiliency, and provide Product and Engineering with clear feedback before customers face similar issues.
• Help solution architects, field teams, cloud and OEM partners, ISVs, and system integrators use the stack successfully through repeatable assets, train-the-trainer sessions, live demos, and direct support on important engagements.
• Collaborate with open-source and cloud-native communities to demonstrate practical integration approaches, address documentation and usability shortcomings, and assist partners in expanding and developing the DSX software stack.
• Listen for recurring problems from customers, partners, the field, and developers. Use those signals to set content priorities and recommend product improvements, then track whether the work reduces deployment time and improves operational success.
• Present your work in customer briefings, partner workshops, industry events, webinars, and internal training. Some travel will be required.

Requirements

• BS or MS in Computer Science, Computer Engineering, Electrical Engineering, or another technical field, or equivalent experience.
• 8+ years of experience in infrastructure engineering, systems engineering, solutions architecture, software engineering, technical marketing engineering, site reliability engineering, or a related role.
• Hands-on experience deploying and operating Linux-based data center, cloud, HPC, or AI infrastructure, including multi-node GPU systems and production operational practices.
• Strong working knowledge of Kubernetes and/or Slurm, including containers, operators, Helm charts, cluster lifecycle, and workload scheduling.
• Experience in several core infrastructure domains, such as bare-metal provisioning, firmware and drivers, compute, Ethernet or InfiniBand networking, storage, identity, multi-tenancy, secrets or certificate management, telemetry, observability, and fleet health.
• Ability to automate deployments and operations through scripting, APIs, configuration management, infrastructure-as-code, Git-based workflows, and CI/CD.
• Examples of technical work for practitioner audiences, such as deployment guides, documentation, reference architectures, code repositories, demos, workshops, blog posts, conference talks, or training. Links to example contributions are greatly appreciated.
• Excellent written, verbal, and visual communication skills. You can explain a complex system and defend a technical recommendation to both business and technical partners.
• Ability to balance multiple projects and constituents, prioritize under tight deadlines, and work well across Engineering, Product, Field, Marketing, and partner teams.

Preferred

• Experience with NVIDIA DSX, DGX systems, DGX Cloud, NVIDIA AI Enterprise, BlueField DPUs, DOCA, or related NVIDIA infrastructure software.
• Experience operating large GPU clusters and diagnosing distributed performance, networking, storage, scheduling, or hardware-health issues.
• Experience with AI training and inference workloads and the requirements for operating them dependably on accelerated infrastructure.
• Experience connecting infrastructure software to facilities or operational technology systems, including power, cooling, building management systems.
• Active participation in cloud-native, HPC, infrastructure automation, or open-source communities, including published examples or project contributions.

Benefits

• NVIDIA offers highly competitive salaries and a comprehensive benefits package. As you plan your future, see what we can offer to you and your family www.nvidiabenefits.com/