Deep Learning Kernel Software Performance Architect

Overview

• NVIDIA is seeking Software Performance Architects to optimize GPU kernel performance for state-of-the-art data-center platforms.
• We build automated, data-driven workflows to detect, explain, and prevent performance regressions across key deep learning workloads, partnering closely with kernel developers, compiler teams, infrastructure, and architecture/performance groups.
• What you'll be doing: Performance analysis, optimization and debugging Build performance narratives using structured methodology: baselines, projections, controlled comparisons, and regression attribution.
• With the methodologies, analyze performance of GPU-accelerated kernels and key deep learning building blocks, identify gaps with baselines or projections, then optimize the kernels' performance to fill the gaps.
• Debug performance issues end-to-end: reproduce, isolate root causes, propose fixes or mitigation paths, and drive closure with the owning teams.
• Automation + regression infrastructure (Python-heavy) Develop and maintain Python-based automation for performance testing and analysis—using modern AI-assisted developer tools (e.g., Cursor/Claude Code/Copilot) to accelerate scripting while keeping code maintainable and reviewable.
• Design and operate performance test workflows: coverage definition, test/workload generation, automated large-scale execution (CI/nightly/on-demand), rerun rules, and reproducibility standards.
• Cross-team collaboration and operating model Work with kernel developers and the compiler teams to ensure performance checks are practical, scalable, and aligned to release needs.
• Work with chip architecture and modeling teams to solidify the performance methodology across chip architecture generations and common Deep Learning operators such as GEMM, Attention, MoE.
• Partner with SWQA and infrastructure teams for execution at scale and reliable pipelines/dashboards.
• Following general software engineering best practices including support for regression testing and CI/CD flows What we need to see: Masters or PhD degree or equivalent experience in Computer Science, Computer Engineering, Applied Math, or related field Strong programming ability in Python plus C/C++ with 2+ working experience (performance-oriented code reading/debugging) Solid fundamentals in computer architecture, parallel programming and performance reasoning (latency/throughput, memory hierarchy, parallelism) to be able to identify bottlenecks, optimize resource utilization, and improve throughput Experience with performance analysis workflows: profiling, measurement methodology, reproducibility, and regression triage.
• Comfortable working across teams and driving issues to decision/closure with clear communication Ways to stand out from the crowd: Experience with high-performance kernels or math libraries (e.g., GEMM/attention, CUTLASS-like concepts) GPU programming/perf experience (CUDA or equivalent parallel programming) Strong ML/DL workload understanding (training/inference shapes, precision modes, perf bottlenecks) Familiarity with simulators/analytical modeling or performance characterization methodology.