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STAFF SOFTWARE ENGR

156191
Beijing Shi, China, China
Dec 17, 2018

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Job Description

Description

  • Xilinx develops highly flexible and adaptive processing platforms that enable rapid innovation across a variety of technologies - from the endpoint to the edge to the cloud. Xilinx is the inventor of the FPGA, hardware programmable SoCs and the ACAP (Adaptive Compute Acceleration Platform), designed to deliver the most dynamic processor technology in the industry and enable the adaptable, intelligent and connected world of the future in a multitude of markets including Data Center (Compute, Storage and Networking); Wireless/5G and Wired Communications; Automotive/ADAS; Emulation & Prototyping; Aerospace & Defense; Industrial Scientific & Medical, and others. Xilinx's core strengths simultaneously address major industry trends including the explosion of data, heterogeneous computing after Moore's Law, and the dawn of artificial intelligence (AI).

    Our global team is growing and we are looking for bold, collaborative and creative people to help us lead the industry transformation to build an adaptable intelligent world. We believe that by embracing diverse ideas, striving for excellence in all that we do, and working together as a unified team, we can accomplish anything. Come do your best work and live your best life as part of the ONEXILINX team!


Algorithm Development
  • Research and develop algorithms including but not limited to face detection, face attribute, face recognition, object detection, model compressing;
  • Work closely with software engineers to deploy algorithms on FPGA;
  • Stay up to date with the latest technology trends;

 

 

Qualifications

  • Masters or above degree, majored in CS, EE, Mathematics or other machine learning related field;
  • Have good knowledge of machine learning, computer vision, and deep learning;
  • Have strong coding skills in C/C++ and Python;
  • Familiar with deep learning framework, TensorFlow/Caffe/Pytorch;
  • Experience in training and optimizing model on large scale dataset;

 

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