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Nvidia Ai Infrastructure Jobs (NOW HIRING)

Nvidia

OR · On-site

$108K - $147K/yr

Joining NVIDIA's DGX Cloud AI Efficiency Team means contributing to the infrastructure that powers our innovative AI research. This team focuses on developing tools for optimizing efficiency and ...

Nvidia

Senior Full-Stack Lead Engineer

Seattle, WA · On-site

The AI Hub team accelerates AI research by ensuring NVIDIA's AI infrastructure is used efficiently, transparently, and at scale. Our primary goal is to build a unified, self-service "single pane of ...

NTT Ltd.

Your day at NTT DATA The Senior Principal AI Infrastructure Architect is a highly skilled and ... Architect reference designs built on NVIDIA DGX/HGX SuperPOD, Dell AI Factory with NVIDIA, Cisco ...

Nvidia

Senior Full-Stack Lead Engineer

Santa Clara, CA · On-site

The AI Hub team accelerates AI research by ensuring NVIDIA's AI infrastructure is used efficiently, transparently, and at scale. Our primary goal is to build a unified, self-service "single pane of ...

Nvidia

NCX Engineer, AI Accelerator

Santa Clara, CA · On-site

Direct experience collaborating with NVIDIA Cloud Partners, hyperscale CSPs, or managed AI cloud platforms, including implementation of NVIDIA reference architectures for AI infrastructure. * Deep ...

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Nvidia Ai Infrastructure information

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How much do nvidia ai infrastructure jobs pay per year?

As of Jun 21, 2026, the average yearly pay for nvidia ai infrastructure in the United States is $154,028.00, according to ZipRecruiter salary data. Most workers in this role earn between $113,000.00 and $197,000.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as an Nvidia AI Infrastructure Engineer, and why are they important?

To thrive as an Nvidia AI Infrastructure Engineer, you need a strong foundation in computer science, cloud computing, and distributed systems, often supported by a relevant degree and experience with large-scale AI workloads. Familiarity with Nvidia GPU technologies, CUDA programming, Kubernetes, and cloud platforms like AWS or Azure is typically required, along with certifications in cloud or AI infrastructure. Strong problem-solving skills, collaboration, and adaptability are essential soft skills for working across interdisciplinary teams and quickly evolving projects. These abilities ensure efficient deployment, scalability, and optimization of AI infrastructure, which are crucial for supporting advanced AI applications.

What is Nvidia AI Infrastructure?

Nvidia AI Infrastructure refers to the hardware, software, and cloud solutions provided by Nvidia to support the development, deployment, and scaling of artificial intelligence applications. This includes high-performance GPUs, networking technologies, data center platforms, and specialized software frameworks such as NVIDIA CUDA and NVIDIA AI Enterprise. Nvidia's AI infrastructure enables organizations to accelerate machine learning, deep learning, and data analytics workloads, both on-premises and in the cloud, delivering efficient and scalable AI solutions.

What are some typical challenges faced when managing AI infrastructure at Nvidia, and how can new team members prepare for them?

Managing AI infrastructure at Nvidia often involves supporting high-performance computing environments, scaling resources for large-scale machine learning workloads, and ensuring system reliability. New team members may face challenges such as optimizing GPU clusters, troubleshooting complex distributed systems, and staying current with rapidly evolving AI frameworks. To prepare, it's helpful to become familiar with Nvidia's hardware ecosystem, cloud-native technologies, and best practices for infrastructure automation. Proactively collaborating with software engineers, data scientists, and IT specialists is also essential for success in this dynamic environment.
Infographic showing various Nvidia Ai Infrastructure job openings in the United States as of June 2026, with employment types broken down into 33% Full Time, and 67% Contract. Highlights an 100% In-person job distribution, with an average salary of $154,028 per year, or $74.1 per hour.

Solutions Architect, AI Factory Infrastructure DevOps

NVIDIA AI

Ashley, OH • On-site

Full-time

Posted 5 days ago

Job description

Job Summary:
NVIDIA AI is at the forefront of the AI computing revolution, building innovative deep learning solutions that reshape industries worldwide. They are seeking a Solutions Architect to join their AI Factory infrastructure deployment team, where the role involves architecting and scaling high-performance AI infrastructure and supporting sales efforts with technical expertise. The position requires strong customer interaction skills and a focus on deep learning inference support.
Responsibilities:
• Help architect and scale high-performance, distributed AI infrastructure on-prem or in the cloud, built with the latest NVIDIA GPU supercomputers for new and existing customers.
• Be a technical specialist on GPU and networking products, directly supporting sales account managers to secure build wins.
• Actively establish and nurture technical relationships with engineers, management, and architects at key customer accounts.
• Identify customer architectures and key product requirements in the CSP/OEM AI market to efficiently implement NVIDIA's solutions.
• Provide on-site support to solve hardware and software problems, with a focus on deep learning inference.
• Lead the product through its entire lifecycle, from design-in to end-of-life, ensuring detailed execution and customer satisfaction.
• Actively maintain the NVIDIA side of infrastructure components and collect findings at the customer site.
• Offer technical and sales training to direct sales teams and channel partners.
• The expected travel requirement is approximately 25-30%.
Qualifications:
Required:
• BS or MS in Engineering, Electrical Engineering, Physics, or Computer Science (or equivalent experience).
• 5+ years of work-related experience in high-tech IT companies with experience in NCP, CSP, site reliability, and virtualization technologies (VMware, Linux KVM).
• 4+ years of working experience with Kubernetes, Slurm, Docker, etc.
• Proficiency with AI tools (Claud, Codex, Perplexity, etc.), Redfish, Grafana, and Prometheus.
• Remarkable talent for effectively handling multiple initiatives and priorities.
• Strong time-management and social skills for coordinating complex projects.
• Excellent written and oral communication skills in English, with the ability to collaborate effectively with both management and engineering teams.
Preferred:
• This role requires hands-on experience and extensive ability to solve problems within the customer infrastructure.
• Kubernetes (K8S) is an infrastructure-orchestration software platform (NVIDIA Mission Control).
• Practical knowledge of NVIDIA systems technology, such as DGX, GB200, and HGX systems, is a huge plus.
• Experience working with OEMs in industrial, military, and ruggedized computing spaces.
Company:
Explore the latest breakthroughs made possible with AI. Founded in , the company is headquartered in Santa Clara, CA, US, , with a team of 10001+ employees. The company is currently Late Stage.

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