Senior GPU Networking Architect

Das ist der Job

ppNVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years.

Darum lohnt es sich

Join our team of engineers developing the software foundation for the largest AI systems globally. /ph3Responsibilities /h3ulliBuild, implement, and optimize GPU communication kernels that underpin collective and point-to-point operations in large-scale AI systems. /liliLeverage deep knowledge of GPU architecture—thread scheduling, memory hierarchy, execution pipelines—to improve kernel efficiency, minimize latency, and overlap computation with communication. /liliDevelop GPU-resident communication primitives and device-side APIs that enable fine‑grained, kernel‑initiated data movement across nodes and accelerators. /liliProfile and tune GPU kernels end-to-end, identifying bottlenecks at the intersection of compute, memory, and network, and driving targeted optimizations. /liliCollaborate with network software, hardware, and AI framework teams to co‑design communication strategies that align with GPU execution patterns and emerging model architectures. /liliBuild proofs‑of‑concept, conduct experiments, and perform quantitative modeling to evaluate and validate new communication strategies before committing them to production. /liliContribute to the evolution of programming models that expose GPU‑aware networking capabilities to application developers. /li /ulh3Qualifications /h3ulli5+ years of hands‑on CUDA programming, including writing and optimizing non‑trivial GPU kernels. /liliM.Sc. or equivalent experience in computer science, computer engineering, or a closely related field. /liliStrong understanding of GPU architecture fundamentals: warp scheduling, shared memory, L2 cache, memory coalescing, occupancy tuning, and asynchronous execution. /liliExperience with systems‑level C/C++ development in performance‑critical environments. /liliFamiliarity with GPU data movement mechanisms such as GPUDirect RDMA and GPU‑initiated communication. /liliAbility to read and reason about GPU performance profiles (e.g., Nsight Compute, Nsight Systems) and translate observations into actionable optimizations. /liliStrong collaboration skills in a multi‑national, interdisciplinary environment. /li /ulh3Ways to Stand Out /h3ulliExperience developing or optimizing communication kernels in libraries such as NCCL, NVSHMEM, or similar GPU‑aware communication frameworks. /liliUnderstanding of distributed deep learning parallelism techniques, including data parallelism, tensor parallelism, pipeline parallelism, expert parallelism, and mixture‑of‑experts parallelism, and the communication patterns they impose on GPU kernels. /liliBackground in RDMA, InfiniBand, high‑speed networking, and GPU system topology, including NVLink, NVSwitch, PCIe, and network fabrics, and their impact on communication kernel design. /liliExperience with overlap techniques such as kernel pipelining, persistent kernels, or cooperative groups to hide communication latency behind compute. /liliProven experience evaluating and optimizing large‑scale LLM training or inference workloads, including hands‑on work with frameworks such as PyTorch, TensorRT‑LLM, or vLLM, and familiarity with emerging serving architectures such as disaggregated serving. /li /ulh3Salary and Benefits /h3pAt NVIDIA, you’ll work alongside colleagues who demonstrate deep expertise and innovative thinking in the industry, pushing the boundaries of what’s possible in AI and high‑performance computing.

NVIDIA offers highly competitive salaries and a comprehensive benefits package. /ppIf you’re passionate about GPU architecture, low‑level kernel optimization, and building the communication fabric for next‑generation AI, we want to hear from you! /p /p #J-18808-Ljbffr It’s a unique legacy of innovation that’s fueled by great technology—and amazing people.

Today, we’re tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world.

Doing what’s never been done before takes vision, innovation, and the world’s best talent. /ph3Overview /h3pWe are looking for a Senior GPU Networking Architect to join our networking software group, bringing strong GPU architecture and programming skills to build and improve GPU communication kernels.

This role links GPU computing with networking by making sure communication primitives are carefully developed alongside GPU hardware capabilities. Your base salary will be determined based on your location, experience, and the pay of employees in similar positions.

For Poland: the base salary range is 292,500 PLN – 507,000 PLN for Level4, and 375,000 PLN – 650,000 PLN for Level5.