STR
Principal Signal Processing Engineer
$206K–$284K
Woburn, MA +1 · Principal · Full-time · Posted Oct 14, 2026
- Location
- Woburn, MA
Woburn, Massachusetts, United States - Experience
- 8+ years on EW or Radar programs; 2+ years in two or more specified technical areas.
- Education
- PhD, MS, or BS in Electrical Engineering, Applied Mathematics, Physics, or a related technical discipline.
About the job
Develops and applies algorithms for radar and RF sensing and counter-sensing, leads research teams, and helps demonstrate prototype system capabilities.
What you'll do
- Engage with customers to understand requirements and shape technical solutions.
- Develop signal processing and machine learning techniques for radar and RF challenges; collaborate on prototype systems.
- Lead multidisciplinary teams and support business development and proposal writing.
What they ask for
- Ability to obtain a Top Secret security clearance; U.S. citizenship is needed by the U.S. Government.
- PhD, MS, or BS in Electrical Engineering, Applied Mathematics, Physics, or a related technical discipline.
- 8+ years of experience on EW or Radar programs, including developing sensor system modes and advanced signal- and data-processing algorithms through testing and verification.
- 2+ years of experience in two or more listed areas, such as RF/radar signal processing, optimization, waveform design, electronic warfare, machine learning, or algorithm development.
Nice to have
- Active Secret or TS/SCI Clearance; demonstrated leadership of complex research, development, and engineering efforts.
- Advanced degree or experience with defense-sector radar/RF technologies, machine learning methods, or translating mission needs into end-to-end solutions.
Pay
$206,000 - $284,000 USD base salary
Tools
MATLAB, Python, C/C++, PyTorch, Ray, TensorFlow/board, MLflow
AI focus
Applies machine learning to radar and RF sensing and counter-sensing; preferred experience includes NLP, image recognition, semantic segmentation, reinforcement learning, Bayesian methods, deep convolutional and graph neural networks.
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