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HPE is seeking an experienced HPC performance engineer who is excited to help drive performance of systems used to solve the world’s biggest problems. As a member of the HPC and AI Solutions and Performance Engineering team, this engineer will benchmark, profile, and tune HPC and AI related applications along with industry-standard benchmarks across HPE’s HPC and AI product portfolio to identify, analyze, and resolve engineering and customer issues. This engineer will have the opportunity to collaborate with leading scientists and engineers, contributing to groundbreaking innovation in HPC and AI. The successful candidate will demonstrate strong technical capability in HPC scientific applications, performance analysis, and HPC architecture, along with a commitment to advancing the performance of HPE supercomputers deployed at customer sites worldwide.
Job Responsibility
Provide pre- and post-sales technical and benchmarking support to enable HPC or AI customer opportunities
Collaborate with HPE product engineering, product management, and marketing to influence current and future product capabilities and direction
Evaluate emerging technologies and assess their impact on product differentiation (performance and features) for key customer applications
Develop and maintain current knowledge of HPE and competitor HPC/AI products and performance optimization techniques to ensure the team continues to deliver high-quality benchmark results
Lead application-focused performance studies and projects, and author technical reports and papers that communicate findings and recommendations
Requirements
Master’s or PhD degree in computer science, engineering, mathematics, or equivalent
7+ years of demonstrated experience with high-end HPC systems and performance analysis/tuning of scalable HPC applications
Excellent analytical and problem-solving skills
Excellent written and verbal communication skills
mastery of English required
U.S. citizenship required
Nice to have
Experience with large-scale HPC systems and software
Experience with large-scale AI systems and software is preferred
Detailed understanding of multiprocessor and GPU hardware architectures, capabilities, and parallel programming techniques (e.g., OpenMP, MPI, CUDA, HIP)
Proficiency with key industry-standard HPC benchmarks (e.g., HPL, HPCG, OSU MPI, NCCL/RCCL) on CPUs and GPUs is preferred
Experience with performance projection methodologies to model performance of alternate system architectures using performance measurement tools and techniques is preferred