Deep Learning Kernel Software Performance Architect

NVIDIA is hiring a Deep Learning Kernel Software Performance Architect in Shanghai, China. Level rates it ; you can apply on NVIDIA.

Level's read on this job

Level rates this role Builds AI ●●●●: the job is building AI systems.
Tools and skills the posting cites include Claude, Copilot and Cursor.
Level classifies it as a senior role.
Level lists 2,700+ other open roles at NVIDIA.

AI in this role

Optimize GPU kernel performance and build data-driven automation for deep learning workloads.

claudecopilotcursorclaude-codepythonccuda

performance-analysisdeep-learningkernel-optimizationgpu

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

How we rate this

Deep Learning Kernel Software Performance Architect at NVIDIA rates 90 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

●●●● Builds AI80 to 100
●●●○ Works on AI60 to 79
●●○○ Uses AI40 to 59
●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.

Key facts

Deep Learning Kernel Software Performance Architect is an open job at NVIDIA.
Level rates this role AI Level 4 of 4: Builds AI.
The role is based in China, Shanghai.
The job was posted on July 9, 2026.
Seniority: senior.
Applications are handled by NVIDIA. Apply on NVIDIA

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

Performance AnalysisDeep learningKernel OptimizationGPUClaudeCopilotCursorClaude Code

Questions you could be asked

Tell me about a project where performance analysis was part of your work. What did you do?
Tell me about a project where deep learning was part of your work. What did you do?
Tell me about a project where kernel optimization was part of your work. What did you do?
Tell me about a project where gpu was part of your work. What did you do?
Walk me through how you've used Claude in your day-to-day work.

Adapt your resume

List these exact terms on your resume: Performance Analysis, Deep learning, Kernel Optimization, GPU, and Claude. An applicant tracking system matches the wording, not the idea.
Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.

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