Principal Architect, Accelerated Computing

What you'll do

• Perform in-depth analysis and optimization to maximize performance on current and next-generation NVIDIA GPUs.
• Create and optimize core parallel algorithms, data structures, and reference codes for GPU solutions.
• Analyze the interplay between hardware and software architectures on core algorithms and programming models.
• Lead hardware design and software engineering teams to guide the direction of accelerated computing.
• Facilitate software-hardware co-design by diving into specific accelerated computing applications.
• Author white papers, conference publications, official blog posts, and patent applications to document work.
• Benchmark and profile workloads on GPU and CPU clusters to characterize performance.

What we're looking for

• MS or Ph.D. in Computer Science, Computer Engineering, or Electrical Engineering, or equivalent experience.
• 12+ years of relevant work experience.
• Strong mathematical fundamentals, including linear algebra and numerical methods.
• Hands-on experience with the massively parallel GPU programming model, such as CUDA or OpenCL.
• Strong knowledge of C and C++ with a solid understanding of software design, programming techniques, and algorithms.
• Experience benchmarking, profiling, and characterizing workloads on GPU and CPU clusters.
• Familiarity with APIs for multi-node communication, like MPI or OpenSHMEM/NVSHMEM (preferred).
• Familiarity with threading APIs for multicore CPUs, Unix-style IPC APIs, and Python (preferred).