Lead Algorithm & Signal Processing Engineer
$157,000–$220,000 year
On-siteWoburn, Massachusetts, United States
Job Summary
Design and apply cutting-edge signal processing and machine learning techniques to solve complex radar and RF sensing and counter-sensing challenges. Implement models and simulations for advanced RF systems while leading small teams and projects to deploy cutting-edge capabilities to prototype systems. Support business development and proposal writing efforts by collaborating with diverse teams to translate mission needs into end-to-end analytical solutions. This role is ideal for a Lead Engineer, though applicants with closely aligned qualifications are encouraged to apply. The position is located in Woburn, MA, or Dayton, OH, and requires a Top Secret security clearance.
Required Qualifications
- Top Secret security clearance
- U.S. citizenship
- BS, MS, or PhD in Electrical Engineering, Applied Mathematics, Physics, or related technical discipline
- 5-7+ years of relevant work experience depending on degree (BS +7 years or MS +5 years)
- Experience in two or more of the following areas: RF/radar signal processing, optimization, waveform design, electronic warfare, machine learning, adaptive signal processing, AI/ML algorithm development, algorithm development, test & evaluation, model & simulation, or systems engineering, test engineering, radar modelling, or RF systems prototyping
- Proficiency in one or more scientific or mathematical programming languages, such as MATLAB, Python, and C/C++
- Strong mathematical, troubleshooting, written, and verbal communication skills
Desired Qualifications
- Active Security Clearance
- Experience with: RF technologies in the defense sector, particularly for sensing and electronic warfare applications including electronic attack, electronic protection, cognitive EW, and advanced electronic support measures techniques and algorithms
- Machine learning tasks such as natural language processing, image recognition, semantic segmentation, reinforcement learning, approaches such as Bayesian, deep convolutional and graph neural network methods, and tools such as PyTorch, Ray, TensorFlow/board, and MLflow
- Interacting with decision-makers and customers to translate mission needs into an end-to-end analytical solution
- Ability to apply novel, innovative and interdisciplinary approaches to challenging engineering problems
- Ability to create and present briefings to customers and senior management to provide both high-level and deep technical solution explanations
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