NVIDIA
Artificial Intelligence
SeniorDeepLearningPerformanceArchitect
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“Senior Deep Learning Performance Architect at NVIDIA. Skills: Deep Learning Performance Architecture, GPU/ASIC architecture, performance modeling, system performance engineering, LLM inference/training optimization. Design and evaluate hardware architectures to improve performance, efficiency, and scalability of production AI workloads. Analyze and optimize large-scale deep learning workloads, especially LLM inference/training in real-world deployments”
Industry & Context.
Identify and resolve system bottlenecks
What They're Looking For.
Must Have
MS or PhD in a relevant field (Computer Science, Electrical Engineering, Computer Engineering, etc) or equivalent experience, 5+ years of hands-on experience in GPU/ASIC architecture, parallel computing, or system performance engineering, Experience with deep learning workloads in production environments (training and/or inference), Proficiency in Python and C++ for building performance models, simulators, or analysis tools, Solid understanding of system architecture: memory hierarchy, data movement, and scalability, Prior experience debugging, profiling, and performance tuning on real systems, Ability to work across team and drive decisions in fast-paced product environments
Nice to Have
Experience translating workload behavior into concrete hardware or system-level improvements, Practical experience with LLM inference optimization: batching, disaggregation, KV-cache management, latency/throughput tuning, Familiarity with production inference systems (e. g. , scheduling, multi-node scaling, resource utilization)
What You'll Do.
Design and evaluate hardware architectures to improve performance
and scalability of production AI workloads
Analyze and optimize large-scale deep learning workloads
especially LLM inference/training in real-world deployments
Build and use performance and power models (Python/C++) to drive architecture and product decisions
Identify and resolve system bottlenecks across compute
Evaluate PPA trade-offs and guide feature prioritization for next-generation GPU/ASIC designs
How You'll Work.
Team & Collaboration
Partner closely with software, systems, and product teams to align hardware capabilities with workload requirements; Ability to work across team and drive decisions in fast-paced product environments
Full Job Description
We are now looking for a Senior Deep Learning Performance Architect! NVIDIA is seeking outstanding Performance Architects to help analyze and develop the next generation of architectures that accelerate AI and high-performance computing applications. Intelligent machines powered by Artificial Intelligence computers that can learn, reason and interact with people are no longer science fiction. GPU Deep Learning has provided the foundation for machines to learn, perceive, reason and solve problems. NVIDIA's GPUs run AI algorithms, simulating human intelligence, and act as the brains of computers, robots and self-driving cars that can perceive and understand the world. Come, join our Deep Learning Architecture team, where you can help build real-time, cost-effective computing platforms driving our success in this exciting and rapidly growing field! **What you’ll be doing** * Design and evaluate hardware architectures to improve performance, efficiency, and scalability of production AI workloads. * Analyze and optimize large-scale deep learning workloads, especially LLM inference/training in real-world deployments. * Build and use performance and power models (Python/C++) to drive architecture and product decisions. * Identify and resolve system bottlenecks across compute, memory, and interconnect. * Evaluate PPA trade-offs and guide feature prioritization for next-generation GPU/ASIC designs. * Partner closely with software, systems, and product teams to align hardware capabilities with workload requirements. **What we need to see:** * MS or PhD in a relevant field (Computer Science, Electrical Engineering, Computer Engineering, etc) or equivalent experience. * 5+ years of hands-on experience in GPU/ASIC architecture, parallel computing, or system performance engineering. * Experience with deep learning workloads in production environments (training and/or inference). * Proficiency in Python and C++ for building performance models, simulators, or analysis tools. * Soli
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