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Senior Staff Software Engineer - Machine Learning 9/17/2017

Xilinx, Inc San Jose, CA

Company
Xilinx, Inc
Job Classification
Full Time
Company Ref #
153555
Location
San Jose, CA
Experience
Mid-Career (2 - 15 years)
Job Type
Regular
Education
Bachelors Degree
AJE Ref #
579546329

JOB DESCRIPTION

APPLY
United States-California-San Jose
Job: Software Engineering
Primary Location: Full-time
Description: Xilinx is the world's leading provider of All Programmable FPGAs, SoCs and 3D ICs. These industry-leading devices are coupled with a next-generation design environment and IP to serve a broad range of customer needs, from programmable logic to programmable systems integration. Our All Programmable devices underpin today's most advanced electronics. Among the broad range of end markets we serve are:

* Aerospace/Defense

* Automotive

* Broadcast

* Consumer

* High Performance Computing

* Industrial / Scientific / Medical (ISM)

* Wired

* Wireless

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 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.



Responsibilities

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

Organization:

Minimum Qualifications

MS/Ph.D. degree in Electrical Engineering or Computer Science with 2 years of industry experience or BA/BS degree in Electrical Engineering or Computer Science with 5 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


Schedule: Sep 15, 2017, 9:46:59 PM
Job Posting: 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.
Unposting Date: false

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 or sexual orientation. The information requested here 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