A leading defence technology company are seeking 3 FPGA Engineers to join its engineering team in Sydney. Reporting to the FPGA Team Lead, this role will focus on developing and expanding drone recognition and disruption algorithms using software-defined radios (SDRs).

The successful candidate will play a key role in implementing complex Digital Signal Processing (DSP) algorithms in production-grade firmware, optimising FPGA designs for performance, stability, and scalability. This is a unique opportunity to work across signal processing, ML, and RF technologies in a high-stakes, mission-driven environment.

Key Responsibilities

• Design, develop, document, and implement FPGA-based systems to support real-time DSP.
• Research and deliver highly optimised RTL implementations of DSP, ML, and statistical algorithms.
• Optimise core FPGA technology for accuracy, performance, and maintainability.
• Scale R&D, development, verification, and validation infrastructure.

Skills & Experience

• Bachelor’s degree in Electronic Engineering or related technical discipline (or equivalent practical experience).
• 2+ years’ professional experience working within an established FPGA team.
• Strong expertise in Verilog, VHDL, or SystemVerilog (design, debugging, verification).
• Hands-on experience with FPGA software tools such as Vivado (preferred), Quartus, Vitis, etc.
• Skilled in synthesis and timing closure for resource-constrained FPGAs.
• Background in high-speed digital design and building custom IP cores.
• Experience with automated verification and validation frameworks.
• Ability to design and implement high-throughput, low-latency, fully pipelined FPGA architectures.
• Proficiency in C/C++ firmware development for FPGA modules, with scripting (Python preferred).
• Comfortable working in Linux environments.

Desirable

• Experience with PCB schematics, board bring-up, and hardware debugging.
• Strong background in Digital Signal Processing.
• RF knowledge, including transmission protocols and principles.
• Understanding of Machine Learning concepts as applied to FPGA implementations.

Please click Apply to send through your details, or email me at [email protected] for further information or you can call me on 0435777965.