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Staff Design Engineer, Machine Learning Accelerator Development

156901
San Jose, CA, United States
May 9, 2019

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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!

Staff Design Engineer, Machine Learning Accelerator Development 
 
Description
You will be part of an R&D team that develops high-performance Machine Learning Inference Accelerators for Xilinx FPGAs.  You will develop solutions for Deep Learning Inference - CNN, RNN, MLP as well as traditional ML inference - clustering, regression. 
 
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.
 
Responsibilities
    • Design and develop FPGA-accelerated Machine Learning Inference solutions
    • Architecture to come-up with custom data flow, custom memory hierarchy for high performance, low latency solution
    • Work with software team for hardware/software co-optimization
    • Work closely with customers to help port their deep learning requirements to their FPGA devices.
     
Minimum Qualifications
    • MS degree in Electrical Engineering or Computer Science with 5+ years of industry experience  or BA/BS degree in Electrical Engineering or Computer Science with 7+ years of industry experience
    • Solid experience in one or more HDL language (System Verilog, Verilog, VHDL), and one or more scripting language (Python, TCL etc.)
    • Experience with design methodologies involving multiple clock domains, clock power management and system debug.
    • Good understanding of common families of Machine Learning models and Machine Learning infrastructure
     
Preferred qualifications
    • Experience with implementing machine learning solutions on GPU, CPU or FPGA
    • Experience in working with standard ML frameworks like Caffe or Tensorflow

Education Requirements
BS/MS degree in Electrical Engineering or Computer Science
Years of Experience

5-7 years

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