About the job
CHAOS Industries builds multi-product solutions for modern defense, supporting warfighters, commercial air operators, and border security teams. Our portfolio centers on Coherent Distributed Networks (CDN™), designed to help users respond quickly and adapt to changing threats.
Founded in 2022, CHAOS Industries has raised $1 billion from 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. Learn more at www.chaosinc.com.
Role Overview
CHAOS Industries seeks a Staff RF Geolocation Engineer to lead the development of passive RF geolocation technology within our electromagnetic warfare products. This role focuses on building, implementing, and validating high-performance localization solutions for distributed systems that detect, characterize, and geolocate non-cooperative RF emitters in complex environments.
The position covers the full algorithm lifecycle: from first-principles formulation and high-fidelity modeling to software integration, calibration, field demonstrations, and validation. Collaboration with Business Development, Production, and cross-functional Engineering teams is central to the work.
What You Will Do
- Design and develop advanced passive RF geolocation algorithms, including TDOA, FDOA, and hybrid architectures for distributed sensor networks.
- Create both coherent and non-coherent passive geolocation and imaging methods, such as phase-aligned multi-node processing for interferometric performance and envelope-based localization techniques.
- Implement statistical signal detection frameworks (Neyman-Pearson, Bayesian, CFAR) to improve detection probability across varied noise, interference, and target scenarios.
- Apply estimation and detection theory, including maximum likelihood estimation and error bound analysis, to build robust and analytically sound localization solutions.
- Model, simulate, and address real-world non-idealities to ensure optimal performance in practical deployments.
