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.
This Principal Architect role leads the research agenda and architectural direction for how NVIDIA’s AI systems communicate at scale—across GPUs, DPUs, NICs, and heterogeneous storage. It requires someone who defines project scope from scratch, publishes original work, and translates research breakthroughs into production-grade software that ships industry-wide!
What you will be doing:
Setting the long-term technical vision for distributed AI communication systems—GPU-to-GPU, GPU-to-storage, and cross-node data movement.
Conducting original research and prototyping next-generation networking solutions over RDMA, NVLink, and GPUDirect.
Driving hardware-software co-optimization with GPU, DPU, NIC, and network switch. Investigating fundamental bottlenecks in communication runtimes for large-scale AI workloads (KV cache transfer, disaggregated prefill/decode, model parallelism).
Integrating networking capabilities into AI serving stacks such as vLLM, SGLang, and TensorRT-LLM.
Publishing findings, representing NVIDIA in industry forums and standards bodies, and mentoring senior engineers across the organization.
What we need to see:
15+ years in systems software and/or networking with deep expertise in high-performance networking (InfiniBand, RoCE, RDMA, NVLink), communication libraries (e.g. NIXL, NCCL, UCX, MPI, NVSHMEM), and GPU accelerated systems, with track record of defining and delivering complex, cross-team technical initiatives from research concept to production.
MS, PhD or equivalent experience in Computer Science, Computer Engineering, Electrical Engineering, or a related field.
Deep understanding of computer architecture, memory hierarchies, DMA engines, and OS-level networking.
Understanding of ML systems concepts—transformer architectures, KV cache mechanics, model parallelism, or distributed training and inference patterns.
Proficiency in programming languages such as C, C++, Rust and Python.
Ways to stand out from the crowd:
Knowledge of ML inference frameworks (vLLM, SGLang, TensorRT-LLM) and their communication requirements.
CUDA programming and NVIDIA GPU architecture expertise.
Proved experience influencing product strategy and technical roadmap at a senior level.
Major open-source contributions.
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 range for this position is 272,000 USD – 431,250 USD.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
- 15+ years of experience in systems software and/or networking
- Deep expertise in high-performance networking, including InfiniBand, RoCE, RDMA, and NVLink
- Experience with communication libraries such as NIXL, NCCL, UCX, MPI, or NVSHMEM
- Experience with GPU-accelerated systems
- Track record of defining and delivering complex, cross-team technical initiatives from research concept to production
- MS, PhD, or equivalent experience in Computer Science, Computer Engineering, Electrical Engineering, or a related field
- Deep understanding of computer architecture, memory hierarchies, DMA engines, and OS-level networking
- Understanding of ML systems concepts, including transformer architectures, KV cache mechanics, model parallelism, or distributed training and inference
- Proficiency in C, C++, Rust, and Python
- Knowledge of ML inference frameworks such as vLLM, SGLang, and TensorRT-LLM
- CUDA programming and NVIDIA GPU architecture expertise
- Experience influencing product strategy and technical roadmaps at a senior level
- Major open-source contributions
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.”
