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Machine Learning Engineer


Headquarters: San Jose, CA
See all Xilinx jobs →


If you are a passionate, innovative and an out-of-the-box thinker that enjoys challenging projects, Xilinx is the right place for you. 
Our global team is growing and we are looking for bold, collaborative, and creative people to help deliver groundbreaking technologies 
that enable our customers to differentiate. Come do your best work and live your best life through collaboration, wellness and giving 
back to your community as a member of the ONEXILINX team.
  • Description 

  • You will be part of an R&D team that develops 
    high-performance low-power FPGA acceleration hardware and software. This 
    position focuses on designing algorithm and infrastructure for high-performance 
    FPGA accelerator for well-known software stacks in the area of Machine 

    You will work on projects critical to Xilinx's growth, 
    with opportunities to move among various teams and projects. You are versatile, 
    display leadership qualities and are enthusiastic to tackle new problems across 
    the full-stack as we continue to push technology forward. Most of all, you are 
    driven to find creative solutions where solutions may not exist yet. 


    • Design and develop 
    FPGA-accelerated Machine Learning solutions 

    • Enable FPGA acceleration of open 
    source deep learning frameworks like: Caffe, MxNet, and Tensorflow 

    • Design and modify machine 
    learning models: reduce computational complexity by model optimization, 
    computation using lower precision arithmetic, data flow reordering for memory 
    bandwidth optimizations 

    • Work closely with customers to 
    port their deep learning requirements to FPGAQualifications 

    Minimum Qualifications 

    • MS/Ph.D. degree in Electrical 
    Engineering or Computer Science or 
    BA/BS degree in Electrical Engineering or Computer Science with 2+ years of 
    industry experience 

    • Solid foundation in data 
    structures, computer arithmetic, algorithms and software design with strong 
    analytical and debugging skills 

    • Good understanding of common 
    families of Machine Learning models and Machine Learning infrastructure 

    Preferred qualifications 

    • Experience with implementing 
    machine learning computation framework on GPU, CPU or FPGA 

    • Experience with developing 
    acceleration application using OpenCL or CUDA 

    • Experience with internals of one 
    of more frameworks like Caffe, MxNet or Tensorflow 

    • Solid engineering and coding 
    skills. Ability to write high-performance production quality code. Experience 
    in C++, Python, and other equivalent languages is a plus 

    • Experience or coursework in FPGA 
    Digital Design or EDA optimization tools