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Solutions Architect, Networking

NVIDIA Corporation

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Skills named in this posting

  • Ethernet
  • C
  • C++
  • Python
Do you want to be part of a team that brings new Artificial Intelligence (AI) technology to NVIDIA’s largest customers? We are looking for an experienced networking Solutions Architect to support accelerated computing networking solutions for AI/ML and HPC on hyperscalers! As part of the NVIDIA Solutions Architecture team, you will be driving end-to-end technology solutions with some of NVIDIA’s most strategic customers with both hardware and software solutions while offering technical insight and support to business and engineering teams based on product strategy. What you’ll be doing: • Working with tech giants to develop and demonstrate solutions based on NVIDIA’s groundbreaking software and hardware technologies. A big part of the day to day job is to help customers debug issues, manage feature request processes, and coordinate the co-engineering program. • Partner with Sales Account Managers and Developer Relations Managers to identify and secure business opportunities for NVIDIA products and solutions. • Be the go-to technical resource for customers building complex AI infrastructure as well as helping them understand performance characteristics for solutions • Work with customers to build PoCs for solutions to address critical business needs • Prepare and deliver technical content to customers including presentations, workshops, etc. • Analyze and develop solutions for customer performance issues for both AI workload and systems performance What we need to see: • BS/MS/PhD in Electrical/Computer Engineering, Computer Science, Physics, or other Engineering fields or equivalent experience. • 5+ years of engineering(performance/system/solution) experience • Expertise with dense datacenter design including networking, compute and storage. Familiarity with Cloud and hybrid cloud Network. • Experience with Ethernet L2-L7 networking protocol stack. • Strong analytical and problem-solving skills • Ability to multitask efficiently in a dynamic environment, ability to work with teams across geographical locations • Clear written and oral communications skills with the ability to effectively collaborate with executives and engineering teams. Ways to stand out from the crowd: • Deep understanding and hands on experience with Ethernet Switch software solution and data center production network. • Strong experience with RDMA/RoCE and smart NICs • Hands on experience with GPU and Networking systems in general including but not limited to performance testing/tuning, benchmarking, etc. • Strong systems engineering, coding, and debugging skills. Including experience with Python, Ansible, Go, C/C++, Bash, Linux and Windows. • Experience in supporting Deep Learning, Machine Learning or HPC networking infrastructures; experience with networking technologies. Your base salary will be determined based on your location, experience, and other objective job-related factors. The expected base salary ranges for this position are: 152,000 USD – 241,500 USD for Level 3; and 184,000 USD – 287,500 USD for Level 4. You will also be eligible for equity and benefits. Applications for this job will be accepted at least until November 6, 2026. This posting is for an existing vacancy. NVIDIA uses AI tools in its recruiting processes. NVIDIA is committed to fostering an inclusive 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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Original posting: jobs.nvidia.com

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RISC-V ecosystem

NVIDIA Corporation in the RISC-V ecosystem

RISC-V International: Premier Board

Founded in 1993, NVIDIA is an AI computing company. The company is headquartered in Santa Clara, California.

  • NVIDIA is bringing CUDA platform support to RISC-V, i.e. allowing a RISC-V CPU to host CUDA GPU applications
  • Publicly credited the RVA23 profile as the enabler: "We wouldn't have considered porting CUDA to RISC-V without it"
  • Active in RISC-V International profile/standards work and a frequent presence in RISC-V ecosystem coverage

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