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Tier-1 employer, mid-level (5+) role in major metros increases applicant competition.
Role requires deep kernel, device-driver, and GPU fabric experience, limiting cross-industry transferability.
Explicit 5+ years plus mandatory kernel, driver, and networking systems expertise creates stringent screening filters.
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Develop and maintain system software enabling efficient communication between GPUs for AI, deep learning, and HPC platforms.
Lead platform bring-up, feature enablement, and end-to-end software validation and debugging on NVLink-based GPU and rack-scale systems.
Address complex issues across software, firmware, networking, and platform environments to optimize performance, reliability, and scalability of GPU networking software stack.
Bachelor's or Master's degree (or equivalent experience) in Computer Science, Computer Engineering, or related field.
5+ years of professional software engineering experience.
Strong C/C++ programming skills with experience in shell scripting; Python and Perl are a plus.
Solid background in Linux development, computer architecture, operating systems, networking fundamentals (TCP/IP, Ethernet, InfiniBand, RDMA), and experience with virtualization technologies (e.g., KVM, QEMU, Hyper-V).
Experience with NVIDIA GPU systems, specifically NVLink, NVSwitch, CUDA, and large-scale AI/HPC clusters.
In-depth understanding of large-scale AI system architectures including PCIe, memory hierarchy, DMA, high-speed interconnects, and distributed training/inference.
Familiarity with server management technologies, data center operations, cluster provisioning, scaling, fleet monitoring, and software quality assurance techniques.