[Skip to main content](#main-content) [OpenNash Jobs](https://jobs.opennash.com/) [Hiring report](/reports/hiring/) · [All NVIDIA jobs](https://jobs.opennash.com/companies/nvidia/) · [Search jobs](/search/) NVIDIA · Multiple locations # Deep Learning Kernel Software Performance Architect Multiple locations Software Engineering Listed on NVIDIA’s official careers site. Listed as of October 20, 2026. Check that it is still open and apply there. [Apply on NVIDIA’s careers site →](https://nvidia.wd5.myworkdayjobs.com/NVIDIAExternalCareerSite/job/China-Shanghai/Deep-Learning-Kernel-Software-Performance-Architect_JR2020693-1) [OpenNash Jobs](https://jobs.opennash.com/) / [NVIDIA](https://jobs.opennash.com/companies/nvidia/) / Deep Learning Kernel Software Performance Architect ## Job description 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 ## At a glance - Posted by the employer: Not stated - First seen by OpenNash: Jul 10, 2026 - Last checked: Oct 20, 2026 - Listed for: 102 days - Location: Multiple locations[Apply on NVIDIA’s careers site →](https://nvidia.wd5.myworkdayjobs.com/NVIDIAExternalCareerSite/job/China-Shanghai/Deep-Learning-Kernel-Software-Performance-Architect_JR2020693-1) ## More jobs at NVIDIA ### [AI Computing Software Development Engineer, TensorRT-LLM](https://jobs.opennash.com/companies/nvidia/jobs/77afa0609aa79b42b8bf/) ### [AI Computing Software Development Intern - 2027](https://jobs.opennash.com/companies/nvidia/jobs/2604940341bdd205a387/) ### [AI Computing Software Development Intern, LLM Inference - 2027](https://jobs.opennash.com/companies/nvidia/jobs/5c2898ad9721f640226a/) ### [AI Computing Software Intern, GPU Kernel Libraries - 2027](https://jobs.opennash.com/companies/nvidia/jobs/e5e58114cef438d1674b/) ### [AI Developer Technology Engineer](https://jobs.opennash.com/companies/nvidia/jobs/452fe02898b03b83dd5e/) ## Similar jobs at other companies ### [Z3_Senior Deep Learning Engineer (Engineering / 3D AI)](https://jobs.opennash.com/companies/ntt/jobs/4060faf277f9048e0d07/) NTT · Bangalore, KA, IN, India ### [Intern- Deep Learning R&D for Automated Driving](https://jobs.opennash.com/companies/qualcomm/jobs/1bd901b22e6940c97f17/) Qualcomm · Seoul, Korea, Republic of ### [Kernel/Hypervisor Engineer, EC2 Deep Learning Accelerators](https://jobs.opennash.com/companies/amazon/jobs/49dd577687aed5163c38/) Amazon · DE, BE, Berlin ### [Senior Deep Learning Researcher – Model Efficiency - Qualcomm - Amsterdam](https://jobs.opennash.com/companies/qualcomm/jobs/f9142cc85ab1ff56cc6e/) Qualcomm · Amsterdam Drenthe, North Holland, Netherlands ### [Internship in Domain Adaptation for Time-Series Using Deep-Learning f/m/d (80-100%)](https://jobs.opennash.com/companies/abb/jobs/486d9fb58cd0bd701a30/) ABB · Baden-Daettwil, Aargau, Switzerland OpenNash lists jobs from employers’ own careers sites and does not handle applications. Dates come from the employer where stated; “first seen” and “last checked” are our own observations. OpenNash Jobs · Official listings with dates and links to the employer. [Weekly hiring report](/reports/hiring/) · [Methodology](/methodology/)