Senior GPU Networking Architect

🕒 March 30

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NVIDIA

WebsiteLinkedInAll Job Openings

10,000+ employees

Founded 1993

🤖 Artificial Intelligence

🎮 Gaming

Artificial Intelligence • Gaming • Automotive

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 that underpin collective and point-to-point operations in large-scale AI systems. • Leverage deep knowledge of GPU architecture—thread scheduling, memory hierarchy, execution pipelines—to improve kernel efficiency, minimize latency, and overlap computation with communication. • Develop GPU-resident communication primitives and device-side APIs that enable fine-grained, kernel-initiated data movement across nodes and accelerators. • Profile and tune GPU kernels end-to-end, identifying bottlenecks at the intersection of compute, memory, and network, and driving targeted optimizations. • Collaborate with network software, hardware, and AI framework teams to co-design communication strategies that align with GPU execution patterns and emerging model architectures. • Build proofs-of-concept, conduct experiments, and perform quantitative modeling to evaluate and validate new communication strategies before committing them to production. • Contribute to the evolution of programming models that expose 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 (e.g., Nsight Compute, Nsight Systems) and translate observations into actionable optimizations. • Strong collaboration skills in a multi-national, interdisciplinary environment.

🏖️ Benefits

• Health insurance • 401(k) matching • Flexible work hours • Paid time off • Remote work options