At Xilinx, we are leading the industry transformation to build an adaptable, intelligent world. ARE YOU bold, collaborative, and creative? We develop leaders and innovators who want to revolutionize the world of technology. We believe that by embracing diverse ideas, pushing boundaries, and working together as ONEXILINX, anything is possible.
Our culture of innovation began with the invention of the Field Programmable Gate Array (FPGA), and with the 2018 introduction of our Adaptive Compute Acceleration Platform (ACAP), has made a quantum leap in capability, solidifying our role as the adaptable platform supplier of choice. From the beginning, we have always believed in providing inventors with products and platforms that are infinitely adaptable. From self-driving cars, to world-record genome processing, to AI and big data, to the world's first 5G networks, we empower the world's builders and visionaries whose ideas solve every day problems and improve people's lives.
If you are PASSIONATE, ADAPTABLE, and INNOVATIVE, Xilinx is the right place for you! At Xilinx, we care deeply about creating significant development experiences while building a strong sense of belonging and connection. We champion an environment of empowered learning, wellness, community engagement, and recognition, so you can focus on work that matters - world class technology that improves the way we live and work. We are ONEXILINX.
• Designing, training and deploying machine learning solutions (with emphasis on neural networks) in pytorch and tensorflow in the context of network applications (for example cyber security, anomaly detection, network intrusion detection, traffic monitoring, traffic prediction)
• If needed, designing, generating and augmenting new datasets for the tasks at hand
• Designing hardware-efficient DNN topologies and other ML solutions with pruning, quantization and hyperparameter optimization
• PhD/MS Degree in Computer Science, Electrical Engineering, Machine Learning or related field
• 5 years or more experience and understanding in the following fields
Comfortable with design & training of neural networks with one or more of the common frameworks
Hardware-efficient ML models or model compression
Performance/compute-cost awareness for ML model
Statistics and classical ML methods
Architectures for dedicated high-speed applications – at minimum basic computer architecture understanding
Networking (5G, Ethernet, TLS, TCP/IP)