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Nvidia Ai Infrastructure Jobs (NOW HIRING)

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 DGX or HGX, Cisco AI infrastructure, Dell PowerEdge XE, Super Micro, Lenovo ThinkSystem, HPE ProLiant Compute DL or Cray. • Deep technical fluency across GPU compute, high-performance ...

NVIDIA DGX or HGX, Cisco AI infrastructure, Dell PowerEdge XE, Super Micro, Lenovo ThinkSystem, HPE ProLiant Compute DL or Cray. • Deep technical fluency across GPU compute, high-performance ...

NCX Senior Engineer

Seattle, WA · On-site

$118K - $163K/yr

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

NVIDIA DGX or HGX, Cisco AI infrastructure, Dell PowerEdge XE, Super Micro, Lenovo ThinkSystem, HPE ProLiant Compute DL or Cray. • Deep technical fluency across GPU compute, high-performance ...

NCX Senior Engineer

Santa Clara, CA · On-site

$122K - $168K/yr

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

NCX Senior Engineer

Santa Clara, CA · On-site

$65 - $83.75/hr

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

In this role, you will focus strictly on commercial execution: driving F5 adoption within NVIDIA's internal infrastructure ("sell-to") and co-selling joint F5-NVIDIA AI solutions to mutual customers ...

Showing results 21-40

Nvidia Ai Infrastructure information

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$80.5K

$154K

$198K

How much do nvidia ai infrastructure jobs pay per year?

As of Aug 10, 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 August 2026, with employment types broken down into 74% Full Time, 22% Part Time, and 4% Contract. Highlights an 66% Physical, 3% Hybrid, and 31% Remote job distribution, with an average salary of $154,028 per year, or $74.1 per hour.

Senior Full-Stack Lead Engineer

NVIDIA Gruppe

Santa Clara, CA • On-site

$224 - $356.50/hr

Other

Posted 5 days ago


Job description

Overview

NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 30 years. Today, we’re at the forefront of AI innovation powering breakthroughs in research, autonomous vehicles, robotics, and more. The DGX Cloud team builds and operates the AI infrastructure that fuels this progress.

We’re looking for a Senior Full‑Stack Software Engineer to join the AI Hub team within the DGX Cloud AI Infrastructure organization. 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 glass” portal that enables AI researchers to efficiently manage, monitor, and optimize their use of Managed AI research Superclusters.

What You’ll Be Doing
  • Lead the architecture and delivery of high‑scale web products across frontend, backend services, and data layers, with clear availability and latency targets (SLOs/SLAs).
  • Own multi‑team initiatives end to end: problem discovery, RFCs/design reviews, phased rollouts, and success metrics tied to product and business outcomes.
  • Drive reliability, performance, and observability improvements to meet exascale standards.
  • Establish engineering standards and reusable platforms/design systems to reduce complexity, support load and long‑term tech debt.
  • Collaborate with NVIDIA AI Research teams to identify pain points and deliver the next generation user experience that accelerates their work.
  • Mentor and sponsor engineers; improve code quality, testing, security, and observability through reviews, pairing, and coaching.
  • Stay ahead of AI/ML infrastructure trends and drive adoption of best practices within the team.
What We Need To See
  • 12+ years of software engineering experience delivering production web systems.
  • Bachelor’s degree or higher in Computer Science or a related technical field (or equivalent experience).
  • Strong cross‑functional collaboration skills, including active listening, translating complex use cases into clear technical requirements, and designing data models aligned with business logic and outcomes.
  • Deep cloud expertise (AWS, GCP, or Azure), infrastructure as code, containers, and orchestration (Docker, Kubernetes), along with mature CI/CD and safe deployment practices.
  • Full‑stack depth: modern SPA frameworks (React/Next.js or Vue/Nuxt), JavaScript/TypeScript, and one or more backend languages (Node.js, Python, and/or Golang).
  • Familiarity with observability stacks such as OpenSearch, Prometheus, Grafana, or Loki.
  • Proficiency in API design (REST), schema evolution, and integration patterns, with a strong commitment to automated testing.
  • Experience building machine learning platforms or self‑service internal infrastructure tools focused on efficiency, resiliency, and observability.
  • Clear written and verbal communication skills, strong problem‑solving ability, and a growth mindset.
  • Experience leveraging AI‑assisted development tools (e.g., Cursor).
Ways to Stand Out from the Crowd
  • Hands‑on ML platform depth (MLE experience or strong familiarity with DL frameworks such as PyTorch, TensorFlow, JAX; distributed training ecosystems like Ray).
  • Datacenter‑scale operational experience, including GPU cluster debugging, performance triage, and root‑cause analysis across complex distributed systems.

At NVIDIA, you’ll be immersed in a diverse, supportive environment where you’re empowered to do your best work. The DGX Cloud AI Infrastructure team is at the core of NVIDIA’s AI efforts building the software that makes scalable research possible. Join us and help power the next wave of innovation. NVIDIA provides competitive salaries and a comprehensive benefits package. Our engineering teams are expanding rapidly due to exceptional growth.

Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 224,000 USD –356,500 USD.

You will also be eligible for equity and benefits.

Applications for this job will be accepted at least until May18,2026.

This posting is for an existing vacancy.

NVIDIA uses AI tools in its recruiting processes.

NVIDIA is committed to fostering a diverse 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.

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