Embedded Software and FPGA Engineer

DroneShield is seeking an Embedded Software and FPGA Engineer with relevant experience to join the development team in Sydney. The role is primarily focused on developing and maintaining DroneShield’s deployed RF real time RF spectrum scanning devices. It represents an exceptional opportunity for a result-driven individual in a new niche but exploding market.

Responsibilities, Duties and Expectations

• System modelling using Matlab/Simulink and implementation of various DSP algorithms on FPGA.
• Implementation of digital filtering, correlation, RF sensing, detection and various signal generation and transmission techniques on FPGA.
• Implementation of signal processing algorithms for the multiple input and multiple output (MIMO) antenna systems.
• Working with Xilinx FPGA using direct code generation tools from Matlab (HDL code generation).
• FPGA resource optimization, integration and testing.
• Development of Python scripts for automated performance testing and configuration management.
• Physical layer RF signal capture and I/Q signal analysis using the dedicated test equipment.
• Work with the engineering team to ensure a reliable and robust end to end system operation.
• Design and develop algorithms and software features from concept to delivery.
• Assist and provide technical in performance testing, product testing, verification and validation.

• BS or MS/PhD degree in electrical engineering or computer science.
• Knowledge on RF propagation and physical layer of various wireless standards (3G, 4G, Wifi and 5G).
• Implementation of DSP techniques with emphasis on physical layer of wireless communication systems.
• 2 years+ of strong FPGA development experience using Verilog (ideal) and Xilinx FPGA software tools such as Vivado.
• Experience with complete product development life cycle.
• Coding experience in fixed point C, Python, and embedded Linux development environments.
• Experience with modelling and code development on SDR platforms.
• Experience developing FPGA implementations of Machine Learning based solutions is ideal.