Senior AI Training Performance Architect
$184,000–$287,500 year
On-siteSanta Clara, California, United States
Job Summary
Understand, analyze, profile, and optimize AI training workloads on state-of-the-art hardware and software platforms by identifying performance bottlenecks across GPU architecture to application code. Prioritize and solve problems within key AI training workloads while implementing production-quality software across NVIDIA's deep learning platform stack from drivers to DL frameworks. Build and support NVIDIA submissions for MLPerf Training benchmarks and implement training workloads in proprietary simulators for future architecture studies. Develop tools to automate workload analysis and optimization workflows. This role directly impacts the hardware and software roadmap for the world's most powerful compute systems.
Required Qualifications
- PhD in CS, EE or CSEE (or equivalent experience)
- 5+ years of relevant experience
- MS with 8+ years of experience
- Strong background in deep learning and neural networks, particularly in training
- Solid understanding of computer architecture and familiarity with GPU architecture fundamentals
- Proven background in analyzing and tuning application performance
- Proven experience with processor and system-level performance modeling
- Proficiency in programming with C++, Python, and CUDA
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