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Nvidia Ai Infrastructure Jobs (NOW HIRING)

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

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 ...

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 ...

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

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 Gruppe

WI · On-site

$120 - $207/hr

Linux, Networking and NVIDIA AI Infrastructure and Operations Certifications such as CCONP, CCIE, JNCIE‑DC/ENT, RHCE, LFCS, NCP‑AII/AIO/AIN. * Shell Scripting (Python, bash, Ansible, yaml, etc.

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Nvidia Ai Infrastructure information

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$80.5K

$154K

$198K

How much do nvidia ai infrastructure jobs pay per year?

As of Sep 5, 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 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.

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.
Infographic showing various Nvidia Ai Infrastructure job openings in the United States as of August 2026, with employment types broken down into 76% Full Time, 20% Part Time, and 4% Contract. Highlights an 66% Physical, 4% Hybrid, and 30% Remote job distribution, with an average salary of $154,028 per year, or $74.1 per hour.

Senior DGX Cloud AI Infrastructure Software Engineer

Nvidia

Redmond, WA

$121K - $165K/yr

Full-time

Re-posted 4 days ago

Nvidia rating

9.6

Company rating: 9.6 out of 10

Based on 18 frontline employees who took The Breakroom Quiz

6th of 247 rated software companies

Job description

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 resiliency of AI workloads - pre-training, post-training, inference. Our objective is to deliver a stable, scalable environment for AI researchers, providing them with the necessary resources and scale to foster innovation. We are seeking an AI infrastructure software engineer to join our team. You'll be instrumental in designing, building, and maintaining AI infrastructure that enable large-scale AI training and inferencing. The responsibilities include implementing software and systems engineering practices to ensure high efficiency and availability of AI systems.

As a senior DGX Cloud AI Infrastructure software engineer at NVIDIA, you will have the opportunity to work on innovative technologies that power the future of AI and data science and be part of a dynamic, diverse, and supportive team that values learning and growth. The role provides the autonomy to work on meaningful projects with the support and mentorship needed to succeed, and contributes to a culture of blameless postmortems, iterative improvement, and risk-taking. If you are seeking an exciting and rewarding career that makes a difference, we invite you to apply now!

What you'll be doing:

  • Develop infrastructure software and tools for large-scale pre-training, post-training, and inference.

  • Develop and optimize tools and libraries to improve infrastructure efficiency and resiliency.

  • Co-design and implement APIs for integration with NVIDIA's resiliency stacks.

  • Enhance infrastructure and products underpinning NVIDIA's AI platforms.

  • Define meaningful and actionable reliability metrics to track and improve system and service reliability.

  • Skilled in problem-solving, root cause analysis, and optimization.

  • Root cause and analyze and triage failures from the application level to the hardware level

What we need to see:

  • Minimum of 8+ years of experience in developing software infrastructure for large scale AI systems.

  • Bachelor's degree or higher in Computer Science or a related technical field (or equivalent experience).

  • Strong debugging skills and experience in analyzing and triaging AI applications from the application level to the hardware level.

  • Experience with observability platforms for monitoring and logging (e.g., ELK, Prometheus, Loki).

  • Proven track record in building and scaling large-scale distributed systems.

  • Experience with AI training and inferencing infrastructure services.

  • Proficiency in programming languages such as Python, C/C++, script languages

  • Experience in quality software engineering practices, including test development, defensive programming, version control, and CI.

  • Excellent communication and collaboration skills, and a culture of diversity, intellectual curiosity, problem solving, and openness are essential.

Ways to stand out from the crowd:

  • Background in working with the large scale clusters

  • Experience in defining and building observability and telemetry software stack

  • Experience with RDMA software stack (NCCL, IB verbs, ucx, libfabrics)

  • Experience and root cause analysis of failures and datacenter scale

  • Good understanding on DL frameworks internal PyTorch, TensorFlow, JAX, and Ray

NVIDIA leads the way in groundbreaking developments in Artificial Intelligence, High-Performance Computing, and Visualization. The GPU, our invention, serves as the visual cortex of modern computers and is at the heart of our products and services. Our work opens up new universes to explore, enables amazing creativity and discovery, and powers what were once science fiction inventions, from artificial intelligence to autonomous cars. NVIDIA is looking for exceptional people like you to help us accelerate the next wave of artificial intelligence.

Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 184,000 USD - 287,500 USD for Level 4, and 224,000 USD - 356,500 USD for Level 5.

You will also be eligible for equity and benefits.

Applications for this job will be accepted at least until April 6, 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.

What Nvidia employees say

Pay

Benefits

Hours and flexibility

Workplace

Get the full story on Breakroom

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About Nvidia

Sourced by ZipRecruiter

NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. It's a unique legacy of innovation that's fueled by great technology--and amazing people. Today, we're tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what's never been done before takes vision, innovation, and the world's best talent.

Industry

Computer and electronic product manufacturing

Company size

10,000+ Employees

Headquarters location

Santa Clara, CA, US

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