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!
Xilinx is seeking a hardware design engineer to join our R&D team developing solutions for machine learning and Data Center acceleration. This is an opportunity to be on the ground-floor for the development of new hardware and software technology for accelerating data center workloads with a particular emphasis on all categories of machine learning. You will be in a team that is architecting future silicon and software for solving web-scale class machine learning technology through to power-efficient embedded machine learning solutions.
Experience with FPGA design flows, FPGA architecture, system design, system integration, DDR memory interfaces and realizing high-performance FPGA-optimized designs and verification methodologies are some of the skills that we are looking for.
Experience implementing digital signal processing algorithms, for example linear algebra functions, FFTs and arithmetic functions employed in machine learning algorithms would be beneficial.
Education level – Masters or PhD preferred
Skills – the candidate would have skills in several, but not necessarily all, of the following
- FPGA design flow
- Verilog and/or VHDL
- Verification methodologies
- Parallel computing
- Scripting languages such as python and tcl
- Knowledge of machine learning datapath architecture would be beneficial
- C/C++ programming skills would be beneficial