Senior GPU Networking Architect

🕒 March 30

🌐 Switzerland, United Kingdom, +1 more countries – Remote

🇨🇭 Switzerland – Remote 🇬🇧 United Kingdom – Remote 🇩🇪 Germany – Remote

⏰ Full Time

🟠 Senior

🏛️ Architect

👻 Ghost score 56%

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NVIDIA

WebsiteLinkedInAll Job Openings

10,000+ employees

Founded 1993

🏥 Healthcare

🏭 Manufacturing

🤖 Artificial Intelligence

Healthcare • Manufacturing • Artificial Intelligence

NVIDIA is a leading technology company specializing in accelerated computing and artificial intelligence. NVIDIA pioneers advancements in graphical processing units (GPUs), cloud computing, data centers, and virtual reality, with a focus on gaming, automotive, healthcare, and robotics industries. The company's innovations, such as NVIDIA Omniverse, transform traditional digital processes by enabling high-fidelity simulations and rendering tasks. Their applications span various industries, from autonomous vehicles using NVIDIA DRIVE to healthcare solutions with NVIDIA Clara, and AI-driven analytics and workflows.

📋 Description

• Build, implement, and optimize GPU communication kernels for collective and point-to-point operations in large-scale AI systems • Leverage GPU architecture knowledge to improve kernel efficiency, minimize latency, and overlap computation with communication • Develop GPU-resident communication primitives and device-side APIs for kernel-initiated data movement across nodes and accelerators • Profile and tune GPU kernels end-to-end, identify compute, memory, and network bottlenecks, and drive targeted optimizations • Collaborate with network software, hardware, and AI framework teams to co-design communication strategies • Build proofs-of-concept, conduct experiments, and perform quantitative modeling to evaluate communication strategies • Contribute to programming models exposing GPU-aware networking capabilities to application developers

🎯 Requirements

• 5+ years of hands-on CUDA programming, including writing and optimizing non-trivial GPU kernels • M.Sc. or equivalent experience in computer science, computer engineering, or a closely related field • Strong understanding of GPU architecture fundamentals: warp scheduling, shared memory, L2 cache, memory coalescing, occupancy tuning, and asynchronous execution • Experience with systems-level C/C++ development in performance-critical environments • Familiarity with GPU data movement mechanisms such as GPUDirect RDMA and GPU-initiated communication • Ability to read and reason about GPU performance profiles using tools such as Nsight Compute and Nsight Systems • Strong collaboration skills in a multi-national, interdisciplinary environment • Experience developing or optimizing communication kernels in libraries such as NCCL, NVSHMEM, or similar GPU-aware communication frameworks • Understanding of distributed deep learning parallelism techniques, including data, tensor, pipeline, expert, and mixture-of-experts parallelism • Background in RDMA, InfiniBand, high-speed networking, and GPU system topology, including NVLink, NVSwitch, PCIe, and network fabrics • Experience with kernel pipelining, persistent kernels, or cooperative groups • Proven experience evaluating and optimizing large-scale LLM training or inference workloads • Hands-on experience with PyTorch, TensorRT-LLM, or vLLM • Familiarity with disaggregated serving architectures

🏖️ Benefits

• Highly competitive salaries • Comprehensive benefits package • Benefits for you and your family