Senior FPGA & DSP Team Lead in Sydney

Senior FPGA & DSP Team Lead in Sydney

Sydney Full-Time 90000 - 110000 $ / year (est.) On-site
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At a Glance

  • Tasks: Lead and innovate in FPGA & DSP engineering for cutting-edge RF detection products.
  • Company: Join DroneShield, a fast-growing leader in counter-drone technology.
  • Benefits: Competitive salary, flexible work environment, and opportunities for professional growth.
  • Other info: Be part of a dynamic team driving innovation in a rapidly evolving industry.
  • Why this job: Make a real impact in the exciting field of RF and AI technologies.
  • Qualifications: Experience in FPGA design, DSP algorithms, and embedded systems required.

The predicted salary is between 90000 - 110000 $ per year.

Drone Shield seeks an experienced FPGA & DSP Team Lead in Sydney to guide the FPGA & DSP Engineering team while contributing hands-on engineering.

The role focuses on RF Detection and Defeat products using SDRs, FPGAs, and advanced signal processing technologies.

The successful candidate will design FPGA-based RF solutions, ARM-based embedded systems, DSP algorithms, and ML implementations, driving high-bandwidth, low-latency architectures within a fast-growing counter-drone company.

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Senior FPGA & DSP Team Lead in Sydney employer: Droneshield

DroneShield is an exceptional employer that fosters a dynamic work culture where innovation and teamwork thrive. As a leader in counter-drone technology, we offer our employees unparalleled growth opportunities through hands-on experience and mentorship in a rapidly evolving industry. Located in the vibrant APAC region, our team enjoys the unique advantage of working on mission-critical projects while collaborating with diverse clients, ensuring a rewarding and impactful career.

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Contact Details:

Droneshield Recruitment Team

We think you need these skills to ace Senior FPGA & DSP Team Lead in Sydney

FPGA Design
DSP Algorithms
RF Detection
SDR (Software Defined Radio)
Embedded Systems (ARM-based)
Signal Processing Technologies
Machine Learning Implementations