NVIDIA seeks a Senior Scale-Up Network System Architect to develop the next generation of NVLink-based scale-up networking for leading AI supercomputing platforms. You will develop the complete architecture of the interconnect that links GPUs within and between racks. This fabric enables sizable AI training and inference clusters to function as a single large accelerator. This system-level role involves teamwork across silicon, firmware, software, and topology development. You will translate workload and customer needs into architectural choices that will be part of NVIDIA’s flagship AI systems for years.
What You'll Be Doing:
- Define end-to-end system architecture for next-generation NVLink scale-up networks, from link and switch behavior through rack- and pod-level topology.
- Convert AI training and inference workload needs (LLM, MoE, and emerging model architectures) into specific networking, bandwidth, latency, and resiliency specifications.
- Drive architecture trade-off studies across performance, cost, power, and reliability, and build the models needed to justify those decisions with data.
- Collaborate closely with ASIC, firmware, software, and systems teams to ensure architecture decisions are implementable and land accurately in silicon and product.
- Develop and use simulation and analytical models to validate architecture choices before committing to silicon.
- Represent system architecture in multi-functional and customer-facing technical discussions, and serve as a technical expert on scale-up network behavior at scale.
What We Need to See:
- Bachelor's, Master's, or PhD in Computer Science, Computer Engineering, Electrical Engineering, or a related field, or equivalent experience.
- 8+ years of industry experience in computer networking, system architecture, or high-performance interconnects.
- Strong grasp of network structure and dynamics at scale, including the impact of topology, congestion, and failure modes on distributed workload performance.
- Experience developing or using simulation/modeling environments to evaluate architecture trade-offs.
- Strong multi-functional collaboration skills — comfortable driving alignment across hardware, firmware, and software teams without direct authority over them.
- Clear technical communication, including the ability to explain complex architecture trade-offs to both engineering and non-engineering collaborators.
Ways to Stand Out from the Crowd:
- Direct experience with NVLink, NVSwitch, or comparable scale-up interconnect technologies.
- Hands-on experience with high-performance networking transports such as RoCE (RDMA over Converged Ethernet), including concepts low-latency transport, congestion control, and lossless fabric build.
- Experience with memory subsystem architecture, including memory hierarchies, cache coherency, and memory technologies such as HBM, DDR, or LPDDR.
- Working knowledge of large-scale AI model architectures and how they map to network requirements.
- Background in HPC or supercomputing-scale interconnect development.
You will also be eligible for equity and benefits.
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.Skills Required
- Bachelor's, Master's, or PhD in Computer Science, Computer Engineering, Electrical Engineering, or a related field, or equivalent experience
- 8+ years of industry experience in computer networking, system architecture, or high-performance interconnects
- Strong understanding of network structure and dynamics at scale, including topology, congestion, and failure impacts on distributed workloads
- Experience developing or using simulation and modeling environments to evaluate architecture trade-offs
- Strong multifunctional collaboration skills across hardware, firmware, and software teams
- Clear technical communication skills for explaining complex architecture trade-offs
- Experience with NVLink, NVSwitch, or comparable scale-up interconnect technologies
- Hands-on experience with RoCE, low-latency transport, congestion control, and lossless fabrics
- Experience with memory subsystem architecture and HBM, DDR, or LPDDR
- Working knowledge of large-scale AI model architectures and network requirements
- Background in HPC or supercomputing-scale interconnect development
NVIDIA Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about NVIDIA and has not been reviewed or approved by NVIDIA.
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Equity Value & Accessibility — Equity awards and a discounted ESPP are highlighted as core parts of total compensation, enabling employees to share in the company’s success. Stock-based compensation and the two-year lookback ESPP are consistently described as especially valuable.
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Healthcare Strength — Health coverage is portrayed as robust, with comprehensive medical, dental, and vision options alongside mental health support and on-site care resources. Employer HSA contributions and wellness perks reinforce the depth of the offering.
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Retirement Support — Retirement programs are depicted as strong, featuring a meaningful 401(k) match with Roth options and support for Mega Backdoor Roth contributions. These elements position long-term savings as a notable advantage of the total rewards package.
NVIDIA Insights
What We Do
NVIDIA’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing — with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world. Today, NVIDIA is increasingly known as “the AI computing company.”
