3 days old

Machine Learning Engineer

San Jose, CA
  • Job Code
Job Title: Machine Learning Engineer 155114

Primary Location United States-California-San Jose
Job: Software Engineering
Schedule: Full-time
Xilinx is the leading provider of All Programmable FPGAs, SoCs, MPSoCs and 3DICs. Xilinx's all-programmable devices are designed into tens of thousands of products that improve the quality of the everyday lives of billions of people worldwide. For over 30 years, Xilinx has been behind some of the greatest advancements in technology and science - from the industry's first fabless semiconductor model to the NASA Curiosity Mars Rover, to today's autonomous vehicles and hyperscale data centers. Xilinx uniquely enables applications that are both software-defined, yet hardware optimized - enabling smart, connected and differentiated applications across technology's biggest megatrends, including Machine Learning, 5G Wireless, Embedded Vision, Industrial IoT and Cloud Computing and more.

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.

  • 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 Learning.


    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 FPGA

  • Qualifications

    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

    Job Posting: Mar 27, 2018, 3:14:59 PM

    Xilinx is an equal opportunity and affirmative action employer. Applicants and employees are treated throughout the employment process without regard to race, color, religion, national origin, citizenship, age, sex, marital status, ancestry, physical or mental disability, veteran status, gender Identity or sexual orientation. The self-identification information requested is not gathered for employment decisions. It is used only for compliance with US Federal laws. Your responses are strictly voluntary, and any information provided will remain confidential. If you choose not to "self-identify", you will not be subject to any adverse treatment.


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