Senior RF Geolocation Engineer

<p>CHAOS Industries is redefining modern defense with a multi-product portfolio that gives the ultimate advantage—domain dominance. The company's products are powered by Coherent Distributed Networks (CDN™), empowering warfighters, commercial air operators, and border protection teams to act faster, adapt rapidly, and stay ahead of evolving threats.&nbsp;</p> <p>CHAOS Industries was founded in 2022 and has raised a total of $1 billion in funding from leading investors, including 8VC, Accel, and Valor Equity Partners. The company is headquartered in Los Angeles, with offices in Washington, D.C., San Francisco, San Diego, Seattle, and London. For more information, please visit <a href="https://www.chaosinc.com">www.chaosinc.com</a>.</p> <p><strong>Role Overview:</strong></p> <p>We are seeking a proactive and detail-oriented Senior RF Geolocation Engineer to lead the development of advanced passive RF geolocation capabilities for our electromagnetic warfare product line. This role is focused on deriving, implementing, and validating high-performance localization solutions that enable CHAOS’s distributed systems to detect, characterize, and geolocate non-cooperative RF emitters in complex environments. The engineer will contribute across the full algorithm lifecycle, from first-principles formulation and high-fidelity modeling through software integration, calibration, field demonstration, and validation, and will collaborate closely with Business Development, Production, and cross-functional Engineering teams.</p> <p><strong>Responsibilities:&nbsp;</strong></p> <p>As a key contributor to the Spectrum Sensing Team, the Senior RF Geolocation Engineer will help redefine our spectrum sensing capabilities around high-confidence passive geolocation. The day-to-day will be diverse, hands-on, and highly technical, with direct impact on next-generation distributed sensing products. The engineer will:</p> <ul> <li>Design and derive advanced passive RF geolocation algorithms from first principles, with emphasis on TDOA, FDOA, and hybrid geolocation architectures across distributed sensor networks</li> <li>Develop coherent and non-coherent passive geolocation and imaging approaches, including phase-aligned multi-node processing for interferometric performance and robust envelope-based localization methods</li> <li>Apply statistical signal detection frameworks, including Neyman-Pearson, Bayesian, and CFAR methodologies, to maximize probability of detection across varying noise, interference, and target conditions</li> <li>Apply estimation and detection theory, including maximum likelihood estimation, error bound analysis, and linear algebraic methods, to formulate robust and analytically defensible localization solutions</li> <li>Model, simulate, and mitigate real-world nonid