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 operating system 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.