Principal Software Engineer - Networking Hyperscale Engineering

About the role

NVIDIA is seeking a Principal Software Engineer to join the US-based Networking Hyperscale Engineering Team. The ideal candidate will work directly with top-tier cloud and AI customers, co-develop software that powers their AI superclusters, and influence NVIDIA's NIC software roadmap.

Responsibilities

• Co-developing NIC software and communication paths with strategic, top-tier customers to enable and scale large AI superclusters.
• Designing and implementing high-performance C/C++ components on Linux using DPDK, kernel-bypass techniques, and RDMA/RoCE.
• Developing and integrating kernel, driver, and NIC firmware features to improve throughput, latency, and reliability for AI workloads.
• Working closely with NCCL and distributed training teams to tune end-to-end collectives performance over NVIDIA networking at scale.
• Owning complex performance and functionality debug with customers and representing the team in cross-org architecture discussions.

Requirements

• 15+ years overall experience in a similar or related systems/networking software role.
• A Bachelor’s, Master’s or PhD in Software Engineering, Computer Science, Computer Engineering, Electrical Engineering, or a related field (or equivalent experience).
• Deep C/C++ expertise, strong Linux systems knowledge, and hands-on experience with kernel networking/RDMA/NIC drivers or DPDK.
• Proven experience developing and debugging network operating systems (NOS) and routing/switching protocols used in AI data centers (for example BGP, ECMP, EVPN/VXLAN).
• Practical experience with DOCA, NIC firmware interfaces, or other hardware-accelerated networking stacks for large-scale systems.
• Excellent communication skills and a track record of effective collaboration with developers, partners, and customers in dynamic environments.

Qualifications

• Deep knowledge of Linux kernel/systems internals, SoC/SmartNIC/NIC embedded systems, and data center switches and NOS.
• Hands-on experience with RDMA/RoCE, GPU-related networking (for example GPUDirect RDMA), and high-performance, low-latency data paths.
• Background optimizing NCCL or other distributed training stacks on large GPU clusters for throughput and tail latency.
• Experience working with hyperscalers or major cloud providers on strategic, performance-critical AI networking deployments.
• Contributions to open-source networking, RDMA, DPDK, kernel, CUDA/NCCL, or related ecosystems.

Benefits

Competitive salaries, generous benefits package, and eligibility for equity. Applications accepted until July 4, 2026. NVIDIA is an equal opportunity employer committed to diversity and inclusion.