NVIDIA
Accelerated High Performance Computing, Scientific Computing, Machine Learning, AI, Datacenter, and Automotive Computing
SeniorAcceleratedComputingArchitect
Neural analysis suggests this role is
optimal for Senior candidates.
“Senior Accelerated Computing Architect at NVIDIA. Skills: Accelerated Computing Architecture, GPU Programming, Performance Optimization, Software-Hardware Co-design. Performing in-depth analysis and optimization to ensure the best possible performance on current and/or next-generation NVIDIA GPUs. Creating and optimizing core parallel algorithms, data structures, and reference codes to provide the best possible solutions for NVIDIA GPUs”
What You'll Achieve.
ensure the best possible performance on current and/or next-generation NVIDIA GPUs; provide the best possible solutions for NVIDIA GPUs
Industry & Context.
logical approach to problem solving
What They're Looking For.
Must Have
MS or Ph. D. in Computer Science, Computer Engineering or Electrical Engineering, or equivalent experience, 6+ years of relevant work experience, mathematical fundamentals, including linear algebra and numerical methods, Hands-on experience with the massively parallel GPU programming model, e. g. CUDA or OpenCL, knowledge of C and C++ with solid understanding of software design, programming techniques, and algorithms, Experience benchmarking, profiling characterizing workloads on GPU and CPU clusters
Nice to Have
Familiarity with APIs for multi-node communication, like MPI or OpenSHMEM/NVSHMEM, Familiarity with threading APIs for multicore CPUs and Unix-style Inter-process Communication (IPC) APIs, Familiarity with Python
What You'll Do.
Performing in-depth analysis and optimization to ensure the best possible performance on current and/or next-generation NVIDIA GPUs
Creating and optimizing core parallel algorithms
and reference codes to provide the best possible solutions for NVIDIA GPUs
Understanding and analyzing the interplay of hardware and software architectures on core algorithms
Diving into accelerated computing applications to facilitate software-hardware co-design
Writing up and presenting your work by writing white papers
conference publications
How You'll Work.
Team & Collaboration
Actively collaborating with the hardware design, software engineering, product, and research teams to guide the direction of accelerated computing
Communication Scope
Good communication skills; writing white papers; conference publications; official blog posts; patent applications
Process & Methodology
good time management, task prioritization skills
Full Job Description
We are now looking for a Senior Accelerated Computing Architect! NVIDIA is developing software and system architectures for accelerated high performance computing, scientific computing, machine learning, AI, datacenter, and automotive computing. This position offers you the opportunity to make a meaningful impact in a fast-moving, technology focused company. **What you 'll be doing:** * Performing in-depth analysis and optimization to ensure the best possible performance on current and/or next-generation NVIDIA GPUs. * Creating and optimizing core parallel algorithms, data structures, and reference codes to provide the best possible solutions for NVIDIA GPUs. * Understanding and analyzing the interplay of hardware and software architectures on core algorithms, programming models, and applications. * Actively collaborating with the hardware design, software engineering, product, and research teams to guide the direction of accelerated computing. * Diving into accelerated computing applications to facilitate software-hardware co-design. * Writing up and presenting your work by writing white papers, conference publications, official blog posts, patent applications, etc. as appropriate. **What we need to see:** * An MS or Ph.D. in Computer Science, Computer Engineering or Electrical Engineering, or equivalent experience * 6+ years of relevant work experience * Strong mathematical fundamentals, including linear algebra and numerical methods. * A passion for performance optimization. * Hands-on experience with the massively parallel GPU programming model, e.g. CUDA or OpenCL. Familiarity with APIs for multi-node communication, like MPI or OpenSHMEM/NVSHMEM, is a plus. * Strong knowledge of C and C++ with solid understanding of software design, programming techniques, and algorithms. Familiarity with threading APIs for multicore CPUs and Unix-style Inter-process Communication (IPC) APIs is a plus. * Familiarity with Python is a plus. * Good communication and organization s
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