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# Senior Deep Learning Performance Architect - LPU

- **Company:** NVIDIA Corporation
- **Location:** United States (Remote available)
- **Experience:** Expert
- **Salary:** $152,000.0 - $241,500.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, C++ (Programming Language), Compilers, Profiling, Nvidia CUDA, Computer Programming, General-Purpose Computing on Graphics Processing Units, Python (Programming Language), Machine Learning, OpenMP, Software Architecture, Systems Architecture, Graphics Processing Unit (GPU), Application Specific Integrated Circuits, Large Language Models, Deep Learning
- **Published:** August 28, 2026
- **Apply:** https://nvidia.wd5.myworkdayjobs.com/NVIDIAExternalCareerSite/job/US-CA-Remote/Senior-Deep-Learning-Performance-Architect_JR2012736

## About the Role

NVIDIA seeks a Senior DL Performance Architect to join our group of pioneers who enjoy pushing AI Inference performance boundaries. Our team focuses on ambitious hardware-software co-design to speed AI Inference workloads. This role gives an outstanding opportunity to develop world-class performance strategies, guide future GPU architecture decisions, and lead AI innovation. If you are passionate about AI efficiency Pareto curves, have a proven record of modeling LLM performance and architecting AI systems, and enjoy optimizing every cycle, this role may be perfect for you!, * A MS or PhD in a relevant field (CS, EE, Math) or equivalent experience, with 5+ years of relevant experience

 * Strong mathematical foundation in machine learning and deep learning
 * Expert programming skills in C, C++, and/or Python
 * Familiarity with GPU computing (CUDA or similar) and HPC (MPI, OpenMP) stack
 * Strong knowledge and coursework in computer architecture

Ways to stand out from the crowd:

 * Background with systems-level performance modeling, profiling, and analysis
 * Experience in characterizing and modeling system-level performance, accomplishing comparison studies, and documenting and publishing results
 * Background in improving AI Inference workloads by developing CUDA kernels or compilers for custom ASIC hardware

## Description

* Design novel GPU and system architectures to advance the forefront of AI Inference performance and efficiency
 * Construct, investigate, and test popular deep learning algorithms and applications
 * Understand and analyze the relationship between hardware and software architectures as it influences future algorithms and applications
 * Build efficient power and performance models of AI inference stack, while capturing minimal but significant information to guide next-gen HW architecture
 * Collaborate across the company to guide the direction of AI, working with software, research, and product teams


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