An applied research team within NVIDIA’s Networking Systems & Software Architecture group is solving some of AI’s hardest infrastructure problems. The team builds systems-level software that moves data between GPUs, nodes, and storage at the speed modern AI demands—spanning low-level transport optimization, hardware-software co-design, and communication frameworks that plug directly into production AI stacks. The team's charter expands into emerging domains including quantum computing interconnects.
The Senior Architect role is to own modules and projects end-to-end—from scoping research questions to shipping production code. It calls for a recognized expert who drives technical decisions, pulls in ideas from research and industry, and regularly prototypes new approaches to prove a point. The work lives at the boundary of applied research and production engineering!
What you will be doing:
Architecting and implementing high-performance communication and memory management libraries for distributed AI
Driving hardware-software co-optimization with GPU, DPU, NIC, and switch teams through GPUDirect RDMA, NVLink, and next-generation interconnects
Profiling and optimizing data movement across GPU memory, system DRAM, NVMe, and network fabrics
Integrating networking capabilities into AI serving stacks such as vLLM, SGLang, and TensorRT-LLM
Contributing to and maintaining open-source projects, mentoring engineers, conducting design reviews, and prototyping experimental technologies to evaluate their viability
What we need to see:
12+ years in systems software and/or networking with demonstrated ownership of complex projects.
MS, PhD or equivalent experience in Computer Science, Computer Engineering, Electrical Engineering, or a related field.
Solid understanding of high-performance networking: InfiniBand, RoCE, RDMA, NVLink, GPUDirect.
Strong C/C++/Rust systems programming with comfort in performance profiling and low-level debugging.
Understanding of ML systems concepts—transformer architectures, KV cache mechanics, model parallelism, or distributed training and inference patterns.
Ways to stand out from the crowd:
Knowledge of ML inference frameworks (vLLM, SGLang, TensorRT-LLM) and their communication requirements.
Knowledge of storage networking (NVMe-oF, GPUDirect Storage, S3).
Background of Reinforcement Learning systems.
With competitive salaries and a comprehensive benefits package, NVIDIA is widely regarded as one of the most desirable technology employers in the world. Our teams are composed of some of the most forward‑thinking and driven engineers in the industry, and we continue to grow rapidly. If you are a senior data engineer passionate about building large‑scale, high‑impact data platforms, we’d love to hear from you.
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:224,000 USD – 356,500 USD for Level 5; and 272,000 USD – 431,250 USD for Level 6.
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
- 12+ years of experience in systems software and/or networking, with demonstrated ownership of complex projects
- MS, PhD, or equivalent experience in Computer Science, Computer Engineering, Electrical Engineering, or a related field
- Strong understanding of high-performance networking, including InfiniBand, RoCE, RDMA, NVLink, and GPUDirect
- Strong C, C++, or Rust systems programming skills
- Comfort with performance profiling and low-level debugging
- Understanding of ML systems concepts, including transformer architectures, KV cache mechanics, model parallelism, or distributed training and inference
- Knowledge of ML inference frameworks such as vLLM, SGLang, and TensorRT-LLM
- Knowledge of storage networking, including NVMe-oF, GPUDirect Storage, and S3
- Background in reinforcement learning systems
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 — Company stock and RSUs have driven exceptional total compensation, with a generous employee stock purchase plan and ongoing equity refreshers playing a central role. Feedback suggests historic share-price gains created significant financial upside for many.
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Healthcare Strength — Multiple plan options (HSA, PPO, and in some locations Kaiser) with in-network preventive care covered at 100% and company HSA contributions indicate a robust offering. Mental-health sessions and broad dental, vision, and disability coverage further strengthen the package.
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Parental & Family Support — Up to 22 weeks of fully paid parental leave, adoption and surrogacy reimbursement, and coverage for IVF and egg freezing are highlighted as standout features. Additional family-care resources and caregiving support broaden the appeal.
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.”
