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Nvidia Ai Infrastructure Jobs (NOW HIRING)

... NVIDIA Dynamo), large-scale foundation model training, and agentic AI pipelines - co-developed with storage and ecosystem partners. * Design and validate storage-optimized AI infrastructure ...

... with NVIDIA Dynamo), large-scale foundation model training, and agentic AI pipelines - co-developed with storage and ecosystem partners. Design and validate storage-optimized AI infrastructure ...

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, the world leader in accelerated computing and AI infrastructure, is seeking a Networking Product Sales Specialist with a proven track record of selling complex AI Infrastructure solutions to ...

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

$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

$107K - $146K/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 ...

$168 - $328/hr

Today, our AI infrastructure powers global intelligence, transforming every industry. Learn more about NVIDIA. #J-18808-Ljbffr

AI Infrastructure Engineer L3

Santa Clara, CA · On-site

$125K - $164K/yr

Deploy and manage NVIDIA GPU infrastructure (A100, H100, L40) and AI accelerator platforms. * Administer Kubernetes GPU clusters using NVIDIA GPU Operator and related technologies. * Install and ...

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 ...

Showing results 41-60

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 Product Architect, Storage

NVIDIA Gruppe

Santa Clara, CA • On-site

$224 - $356.50/hr

Other

Posted 17 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

Role Overview

As an AI Storage Platform Architect at NVIDIA, this position will be the linchpin between cutting‑edge hardware platforms and real‑world AI deployments - translating the capabilities of Rubin GPUs, Vera CPUs, BlueField DPUs, NVLink fabric, and Spectrum‑X networking into validated, production‑ready blueprints. Work hand‑in‑hand with storage ecosystem partners to co‑develop reference architectures for the NVIDIA AI Data Platform and beyond, ensuring that every layer of the stack—compute, fabric, memory, and storage—is optimized for modern AI workloads.

What you’ll be doing
  • Architect end‑to‑end reference architectures for disaggregated inference (aligned with NVIDIA Dynamo), large‑scale foundation model training, and agentic AI pipelines – co‑developed with storage and ecosystem partners.
  • Design and validate storage‑optimized AI infrastructure, including KV Cache tiering strategies, checkpoint acceleration, and high‑throughput dataset pipelines that leverage RDMA and NVMeoF fabrics.
  • Define system‑level architectures spanning Rubin graphics processors, Vera central processing units, BlueField data processing units, NVLink interconnects, and Spectrum‑X Ethernet to improve efficiency across the full AI lifecycle.
  • Develop and publish reference architectures, whitepapers, and deployment guides for the NVIDIA AI Data Platform and partner‑integrated solutions.
  • Drive prototyping, benchmarking, and performance validation of AI infrastructure at scale – diagnosing bottlenecks across compute, networking, and storage layers.
  • Leverage DOCA to architect DPU‑offloaded data services including storage acceleration, telemetry, security enforcement, and network virtualization.
  • Collaborate with RAG and autonomous AI teams to build retrieval‑optimized storage architectures, including vector database integration, low‑latency object access patterns, and inference‑aware caching.
  • Partner with customers and collaborators in the ecosystem to co‑innovate, deliver proof‑of‑concepts (POCs) and MVPs that demonstrate end‑to‑end AI platform performance leadership.
What we need to see
  • 12+ years of experience architecting datacenter‑scale AI, HPC, or storage infrastructure as a Principal Architect, Solutions Architect, Principal Engineer, or equivalent.
  • Bachelor’s in Computer Science or related field (or equivalent experience).
  • Deep expertise in AI infrastructure build, including disaggregated inference architectures, LLM training pipelines, and autonomous AI system patterns.
  • Hands‑on experience with RDMA (RoCEv2/InfiniBand), high‑performance storage protocols (NVMeoF, GPFS, Lustre, or S3‑compatible object storage), and low‑latency fabric design.
  • Strong understanding of KV Cache management strategies, including tiered memory/storage hierarchies for inference optimization.
  • Familiarity with Retrieval‑Augmented Generation (RAG) architectures and the storage, indexing, and retrieval patterns they demand at scale.
  • Experience with NVIDIA DOCA or equivalent DPU/SmartNIC programming frameworks for offloading data plane and storage services.
  • Proven foundation in networking: Spectrum‑X Ethernet, InfiniBand, NVLink Switch fabrics, congestion control, and datacenter topologies.
Ways to stand out from the crowd
  • Proven experience designing reference architectures jointly with storage or infrastructure OEM partners (e.g., NetApp, DDN, VAST, Pure Storage, Dell or similar).
  • Hands‑on deployment experience with disaggregated inference systems, including prefill/decode separation, KV Cache offload, and request routing.
  • Deep familiarity with NVIDIA Grace‑Hopper, Grace‑Blackwell, or upcoming Vera‑Rubin platforms and their system‑level implications for AI workloads.
Compensation & Benefits

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.

Equal Employment Opportunity

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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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