Signal Processing Engineer RF/RADAR

Signal Processing Engineer (RF / Passive Sensing) WayWave Inc. (spinout of A2 Labs) Arlington, VA, onsite with occasional travel. Full-time.

WayWave is building next-generation airspace awareness with passive sensing. The role uses existing RF signals, including satellite communications and other non-cooperative emitters, as opportunistic sources for detection and tracking. The engineer designs, develops, and deploys algorithms that extract actionable intelligence from complex RF environments, from simulation through field testing to operational integration.

Responsibilities

• Develop signal processing algorithms for detection, localization, and tracking of airborne objects from passive RF data.
• Design ways to exploit non-cooperative signals for sensing and situational awareness.
• Process time-series and spectral RF data in noisy, interference-rich environments.
• Build and validate models for waveform characterization, propagation, and target signatures.
• Contribute to multi-sensor fusion of RF, geospatial, and other data.
• Support field tests, data collection, and performance evaluation.
• Integrate algorithms with software, hardware, and systems engineers.
• Iterate on experimental results and explain findings to technical and non-technical people.

Qualifications

• U.S. citizenship required.
• Able to obtain a security clearance. An active clearance is preferred, not required.
• Bachelor's or master's in electrical engineering, computer engineering, applied math, physics, or a related field.
• 3+ years in signal processing, RF, or a related domain.
• DSP foundation: filtering, spectral analysis, detection theory.
• Real-world data, not only simulation.
• Python, MATLAB, or similar.
• Comfortable in a fast, collaborative environment.

Desired

• Active clearance.
• RF exploitation, passive radar, or remote sensing.
• Satellite sources such as Starlink or OneWeb.
• Geolocation (TDOA, FDOA, multilateration) and tracking (Kalman, multi-target).
• SDRs and RF data collection.
• Machine learning on signals or time series.
• Deployed systems or field campaigns.