CUDA Development Engineering
C P Careers Private Limited
Chennai, Tamil Nadu, in
5d ago
source : JobSearchine

MUST HAVES

Education / qualification : IIT, IIIT, NIT

Must have experience in GPU, CUDA

Must have experience in Image Processing

Must have experience in Signal Processing, Deep Learning, Parallel Programming

  • STRICT NO NOS :
  • Nvidia

    Key Responsibilities :

  • As part of KLA algorithms team, the job entails understanding core algorithms that have to expressed in various parallel computing constructs particularly HPC accelerators such as GPUs.
  • The first step in optimizing will be to theoretically model break-down of our AI algorithms and model it in terms of available bandwidth, computational FLOPS etc.
  • The implementation steps will include CUDA level programming along with performance tuning to ensure that we can come close to achieving the theoretical model.
  • The developer will be exposed to a variety of image processing, signal processing and deep learning loads that have to be optimized.
  • A complimentary stage of optimization includes exploring existing libraries and programming in higher level constructs such C++ Parallel programming.
  • While the initial focus of the team will be on NVIDIA GPUs, the R&D team will also be looking at other GPU accelerators from other vendors as well as FPGA acceleration.
  • You will collaborate with peer researchers in parallel computing areas and with algorithm teams in product groups.

    Minimum Qualifications :

  • New / Recent College graduates in Ph.D. (preferred), Dual Degree MS in EE, CS or CSE. Bachelors graduates will also be considered.
  • A researcher who has a strong foundation in computer architecture, and in particular with a focus on high performance parallel processing at the device level (GPUs or CPUs / SIMD or FPGAs).
  • The researcher should have a strong mental model of computational loads and mapping different algorithms to parallel architectures.
  • Proficient in programming skills in C / Modern C++ and Python.
  • Experience in analyzing and tuning applications using profiling tools such as NSIGHT or VTUNES.
  • Good understanding and exposure to the Linux operating system at the user level.
  • Exposure to multiprocessor and multithreading concepts
  • Some familiarity with GPU programming such as CUDA, OpenCL or SYCL.
  • The position also requires a person with significant communication, initiative and the ability to navigate from relatively high-level requirements to low level computational models.
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