Research and develop techniques to GPU-accelerate high-performance workloads at the intersection of AI and financial markets
Work directly with other technical experts in their fields (industry and academia) to perform in-depth analysis and optimization of complex AI and HPC workloads to ensure the best possible performance on modern CPU and GPU architectures
Publish and present discovered optimization techniques in developer blogs or relevant conferences to engage and educate the Developer community
Influence the design of next-generation hardware architectures, software, and programming models in collaboration with research, hardware, system software, libraries, and tools teams at NVIDIA
Requirements
Pursuing or recently completed a BS, MS or PhD in Computer Science, Computer Engineering, or related field
Relevant work or research experience
Experience improving the performance of large computational applications used by financial institutions
Understanding of linear algebra
Programming fluency in C/C++ with a deep understanding of algorithms and software design
Hands-on experience with low-level parallel programming, e.g., CUDA, OpenACC, OpenMP, MPI, pthreads, TBB, etc.
Experience with CPU/GPU architecture fundamentals
Good communication and organization skills, with a logical approach to problem solving, and prioritization skills.