We are looking for a first-class Deep Learning Performance architect to join in 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 co-design performance-centric solutions.
End-to-End Optimization: Create benchmarks to validate performance improvements across AI/HPC workloads and present actionable insights.
What we need to see:
BS/MS+ in relevant discipline (CS, EE, Math)
Proficiency in C/C++ (performance-critical coding) and Python (automation/scripting, and AI/ML frameworks)
Strong grasp of computer architecture (pipelines, memory hierarchies) and Operating System fundamentals
Understand machine learning and data analysis basics, LLM techniques such as prompt engineering, fine-tuning, vector databases
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 developing HW performance debugging and analysis tools
Familiar with System Software Stack(like CUDA Driver), CUDA kernel optimization and understand GPU architecture
Familiarity with GPU performance profiling tools like Nsight System, Nsight Compute
Practical experience or projects demonstrating LLM-based code generation, automated data analysis, or workflow assistants. Prior experience with agentic LLM frameworks like Langchain and LLamaIndex.
Full-Stack Versatility: Skills in JavaScript, SQL, or UI/UX design for tool interfaces.