$160,000 – $322,000
Listed on NVIDIA’s own careers site. You apply with them directly — we never stand between you and the employer.
What this role is
This role involves building and validating complete AI infrastructure software stacks on multi-node GPU systems, then transforming those deployments into technical content, demos, and training for NVIDIA's customers and partners. It suits engineers who combine deep infrastructure expertise with strong communication skills and enjoy translating complex systems into practical guidance for practitioners.
Our summary, not NVIDIA’s wording. The full posting is on their site.
Skills this role names
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What they ask for
Required
- BS or MS in Computer Science, Computer Engineering, Electrical Engineering, or related technical field, or equivalent experience
- 8+ years in infrastructure engineering, systems engineering, solutions architecture, software engineering, technical marketing engineering, SRE, or related role
- Hands-on experience deploying and operating Linux-based data center, cloud, HPC, or AI infrastructure with multi-node GPU systems
- Strong knowledge of Kubernetes and/or Slurm with containers, operators, Helm charts, cluster lifecycle, and workload scheduling
- Experience across multiple infrastructure domains: bare-metal provisioning, firmware, drivers, compute, networking, storage, identity, multi-tenancy, secrets management, telemetry, observability, fleet health
- Ability to automate deployments through scripting, APIs, configuration management, infrastructure-as-code, Git workflows, and CI/CD
- Portfolio of technical work for practitioner audiences such as deployment guides, documentation, reference architectures, code repositories, demos, blogs, or conference talks
- Excellent written, verbal, and visual communication skills
- Ability to balance multiple projects, prioritize under deadlines, and work across Engineering, Product, Field, Marketing, and partner teams
Nice to have
- 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 their operational requirements on accelerated infrastructure
- Experience connecting infrastructure software to facilities or operational technology systems including power, cooling, and building management systems
- Active participation in cloud-native, HPC, infrastructure automation, or open-source communities with published examples or contributions