We are looking for a first-class Deep Learning Performance architect to join us to shape the performance analysis infrastructures for GPUs. We build cutting-edge analysis tools and visualization frameworks that empower engineers to optimize GPU performance for Deep Learning and HPC workloads—spanning pre-silicon architectural exploration to post-silicon validation and optimization. Your work will directly shape the tools that define how NVIDIA GPUs are analyzed, tuned, and scaled for next-gen AI systems, and impact the next-gen GPUs architectures.   What you'll be doing: + Architect Performance Tooling : Develop infrastructure tools/libraries for GPU performance analysis, visualization, and automated workflows used across GPU SW/HW development life cycle. + Unlock Architectural Insights : Analyze GPU workloads to identify bottlenecks and define new hardware profiling features that enhance perf debug and profiling capabilities. + AI-Powered Automation : Build AI/ML-driven tools to automate performance analysis, generate perf optimization guidance, and improve user experience of profiling infrastructure. + Cross-Stack Collaboration : Partner with kernel developers, system software teams, and hardware architects to support performance study, improve CUDA software stack, and co-design performance-centric solutions for current and next-generation GPU architecture   What we need to see: + BS+ in Computer Science , Electronic Engineering or related (or equivalent experience) + 4 + years of software development + Strong software skill in design, coding (C++ and Python), analytical and debugging in low-level program + Strong grasp of computer architecture (pipelines, memory hierarchies) and o perating s ystem fundamentals + Experience with performance modeling, architecture simulation, profiling, and analysis. + Self-starter who thrives in dynamic environments and manages competing priorities effectively.   Ways to stand out from the crowd: + Experience with building performance debugging and analysis tools on silicon and simulators. Experience of developing application snapshot and replay tool is a big plus. + Familiar with CUDA System Software Stack( e .g., CUDA Driver /Runtime APIs ), CUDA kernel optimization and understand GPU architecture + Familiarity with GPU performance profiling tools like Nsight System, Nsight Compute , NVTX, etc , or experience for developing similar tools for other processors. + Practical experience or projects demonstrating AI/ML -based code generation, automated data analysis, or workflow assistants.