About the job
At CHAOS Industries, we are revolutionizing modern defense with our innovative multi-product portfolio that provides unmatched domain dominance. Our cutting-edge products leverage Coherent Distributed Networks (CDN™), enabling warfighters, commercial air operators, and border security teams to respond swiftly, adapt dynamically, and maintain an edge over evolving threats.
Founded in 2022, CHAOS Industries has secured $1 billion in funding from esteemed investors such as 8VC, Accel, and Valor Equity Partners. Headquartered in Los Angeles, we have additional offices in Washington, D.C., San Francisco, San Diego, Seattle, and London. Discover more about us at www.chaosinc.com.
Role Overview:
We are on the lookout for a dedicated and meticulous Staff RF Geolocation Engineer who will spearhead the creation of advanced passive RF geolocation technologies within our electromagnetic warfare product suite. This pivotal role focuses on developing, implementing, and validating high-performance localization solutions that empower CHAOS’s distributed systems to detect, characterize, and geolocate non-cooperative RF emitters in intricate environments. You will engage in the complete algorithm lifecycle, from first-principles formulation and high-fidelity modeling to software integration, calibration, field demonstrations, and validation, while closely collaborating with Business Development, Production, and cross-functional Engineering teams.
Responsibilities:
As an essential member of the Spectrum Sensing Team, the Staff RF Geolocation Engineer will enhance our spectrum sensing capabilities centered around high-confidence passive geolocation. This role is diverse, technical, and hands-on, directly influencing the next generation of distributed sensing products. You will:
- Design and develop advanced passive RF geolocation algorithms based on first principles, focusing on TDOA, FDOA, and hybrid geolocation architectures across distributed sensor networks.
- Create coherent and non-coherent passive geolocation and imaging methodologies, including phase-aligned multi-node processing for interferometric performance and robust envelope-based localization techniques.
- Implement statistical signal detection frameworks, including Neyman-Pearson, Bayesian, and CFAR methodologies, to enhance detection probability across diverse noise, interference, and target conditions.
- Utilize estimation and detection theory, including maximum likelihood estimation and error bound analysis, to devise robust and analytically sound localization solutions.
- Model, simulate, and address real-world non-idealities, ensuring optimal performance in practical settings.