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Design and implement DFT solutions (test access, IO/memory BIST, scan compression), develop DFT methodologies, verify test patterns on multi-million-gate designs, support silicon bring-up on ATE, and mentor junior engineers.

The Senior Solutions Architect will lead software development, optimize CI/CD systems, develop cloud infrastructure solutions, and collaborate across NVIDIA teams.

Design and implement high-performance communication protocols and algorithms for NVIDIA networked supercomputers and datacenters. Develop production-grade communication frameworks and applications, optimize performance and scalability, and collaborate with networking chip design teams on hardware/software co-design.

Lead functional safety analysis for autonomous vehicle compute and communications platforms. Perform fault-tree and model-based analyses, derive diagnostic measures, develop automation for multi-layer system analysis, interpret safety documentation, and integrate system/software/hardware safety methodologies to meet hardware safety metrics.

Lead development and improvement of RTL2GDS implementation methodology for advanced-node CPU designs. Drive PPA optimization, evaluate EDA tools, run quantitative flow experiments, build automation in Python/TCL/Perl, collaborate with architecture/RTL/DFT teams, and implement data-driven and AI-assisted flow optimizations to improve silicon QoR and engineering throughput.

Lead research and design of RDMA-based transport protocols for large-scale AI systems. Architect frameworks for programmable networking devices, build prototypes and simulations, provide system-level guidance to customers, and collaborate across teams to co-design the software and hardware stack for high-performance distributed GPU workloads.

Support installation and customization of foundry techfiles, debug design/tool/techfile errors, maintain and support EDA/CAD tools (Virtuoso, IC-Manage, DRC/LVS, extraction, SPICE), work with vendors, and develop automation scripts and infrastructure using Perl, Cadence SKILL, and C++ to improve custom circuit designer productivity.

Design and implement an LLVM-based JIT backend for NVIDIA GPUs, develop compiler passes and optimizations to meet performance and compile-time targets, collaborate with hardware and application teams to resolve issues, apply modern compiler technologies, and influence future GPU architectures.

The role involves performing data analysis for datacenter applications, using machine learning for GPU systems, and providing insights on trends to enhance workflows.

As a Principal Software Engineer, you will lead AI networking system transformations, manage complex customer engagements, and influence NVIDIA's networking technologies.