Deep Learning Kernel Software Performance Architect

Responsibilities

• 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.

Languages