NVIDIA’s Performance Group is seeking a technical NIC & DPU Performance Research & Analysis Engineer to drive end-to-end performance strategy, characterization, optimization and HW offload accelerations for next-generation network interfaces and Data Processing Units (DPUs).
In this role, you will analyze and accelerate critical networking and storage technologies and protocols powering multi nodes AI clusters. You will profile performance across the complete hardware-to-software execution stack—stretching from DPU Arm CPU subsystems and PCIe to high-speed RDMA, SPDK, and DOCA acceleration pipelines.
What You’ll Be Doing:
- Data-Path Optimization: Drive end-to-end benchmark plans, performance characterization, expectations and optimizations for NICs and DPUs deployed in distributed AI and cloud data centers.
- Full-Stack Profiling: Profile low-level memory, PCIe, host CPU, and DPU ASIC execution paths to identify micro-architectural stragglers, queue bottlenecks, and tail-latency outliers.
- Storage & Network Acceleration: Hands-on acceleration and offloading of storage (SPDK, NVMe-oF, VirtIO) and networking protocols (RDMA/RoCEv2, TCP/IP, DOCA).
- AI-Assisted Workflow Automation: Leverage AI agents, autonomous scripts, and automated regression frameworks to scale continuous performance evaluation.
- Cross-Functional RCA: Partner closely with Architecture, ASIC, Firmware, and Software engineering groups to conduct rigorous Root Cause Analysis (RCA) and establish performance targets for upcoming pre- and post-silicon platforms.
What We Need to See:
- B.Sc. or M.Sc. in Computer Science, Computer/Electrical Engineering, Software Engineering, or equivalent practical experience.
- 5+ years of experience in low-level networking, storage acceleration, and systems-level performance tuning in cloud or HPC environments.
- Direct hands-on experience with at least two of the following: DOCA, SPDK, VirtIO, RDMA/RoCEv2, TCP/IP.
- Strong programming proficiency in C, Python, and Linux shell scripting (Bash).
- Micro-architectural understanding of modern CPU/ASIC memory hierarchies, PCIe Gen 5/6 interconnects, and Linux kernel networking stacks.
- Proven track record using AI coding agents or automated script generators to streamline daily engineering workflows.
Ways to Stand Out from the crowd:
- Deep hands-on experience tuning NVIDIA BlueField DPUs, ConnectX SmartNICs, or custom hardware offload engines.
- Mastery of the full SPDK & NVMe-oF (NVMe over Fabrics) storage stack over high-speed RDMA transport layers.
- Comprehensive system-level understanding across x86 (Intel/AMD) and Arm architectures, PCIe topology, and GPUDirect Storage (GDS).
- Proven experience isolating stragglers and tail latency in multi-tenant, cloud-scale environments.
Skills Required
- Bachelor’s or master’s degree in Computer Science, Computer Engineering, Electrical Engineering, Software Engineering, or equivalent practical experience
- 5+ years of experience in low-level networking, storage acceleration, and systems-level performance tuning in cloud or HPC environments
- Hands-on experience with at least two of DOCA, SPDK, VirtIO, RDMA/RoCEv2, or TCP/IP
- Strong proficiency in C, Python, and Linux shell scripting with Bash
- Understanding of modern CPU and ASIC memory hierarchies, PCIe Gen 5/6 interconnects, and Linux kernel networking stacks
- Experience using AI coding agents or automated script generators to streamline engineering workflows
- Hands-on experience tuning NVIDIA BlueField DPUs, ConnectX SmartNICs, or custom hardware offload engines
- Experience with the SPDK and NVMe-oF storage stack over RDMA transport layers
- System-level understanding of x86 Intel/AMD and Arm architectures, PCIe topology, and GPUDirect Storage
- Experience isolating stragglers and tail latency in multi-tenant, cloud-scale environments
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.”
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