Performance Engineer jobs in Yokneam

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.

NVIDIA is hiring exceptional software engineers to build and optimize the core inference infrastructure for large language models. Join the TensorRT‑LLM team - the group defining how generative AI performs at global scale on NVIDIA GPUs. We’re looking for engineers who love squeezing every drop of throughput, memory efficiency, and scalability out of modern model runtimes. Your work will directly shape the frameworks behind state‑of‑the‑art LLM inference used across NVIDIA and the AI community. Join us to redefine what “fast” means for LLM inference - building the frameworks that power the next generation of generative AI at scale. What you'll be doing: • Design, implement, and optimize high‑performance inference pipelines for large language models running on GPUs • Profile and tune model execution across the stack - from scheduler design to kernel fusions and everything in-between • Design and experiment with memory management strategies for improved memory bandwidth optimization and cache efficiency • Innovate and Implement cutting-edge techniques such as Speculative Decoding, Context Caching, and FP8/INT4 quantization to push the boundaries of tokens-per-second-per-watt • Develop and maintain benchmarking and testing systems that quantify latency, utilization, and efficiency What we need to see: • Bachelor's, Master's, or higher degree in Computer Engineering, Computer Science, Applied Mathematics, or related computing-focused degree (or equivalent experience) • 5+ years of relevant software development experience. • Excellent Python programming skills, software design, and software engineering skills • Experience working with deep learning frameworks like PyTorch and HuggingFace • Experience profiling and debugging performance at all levels - Python runtime, PyTorch internals, and GPU utilization metrics • Awareness of the latest developments in LLM architectures and LLM inference techniques • Proactive and able to work without supervision • Excellent written and oral communication skills in English Ways to stand out from the crowd: • Contributions to inference frameworks such as TensorRT‑LLM, vLLM, SGLang, or similar systems • Demonstrated expertise in performance modeling, memory optimization, distributed model execution or GPU execution workflows • Hands‑on experience with NVIDIA profiling tools (Nsight Systems, PyTorch Profiler, custom benchmarking harnesses) • Strong grasp of the trade‑offs shaping inference efficiency: compute vs. memory, scheduling vs. batching, latency vs. throughput Widely considered to be one of the technology world’s most desirable employers, NVIDIA offers highly competitive salaries and a comprehensive benefits package. As you plan your future, see what we can offer to you and your family www.nvidiabenefits.com. #LI-Hybrid