Senior RF Geolocation Engineer

CHAOS Industries•Columbia, WA

3d•$110,000 - $151,000•Onsite

About The Position

CHAOS Industries is seeking a proactive, detail-oriented Senior RF Geolocation Engineer to lead development of advanced passive RF geolocation capabilities for its electromagnetic warfare product line. The role develops and validates high-performance localization solutions that allow distributed systems to detect, characterize, and geolocate non-cooperative RF emitters in complex environments. The engineer contributes throughout the algorithm lifecycle, from first-principles formulation and high-fidelity modeling to software integration, calibration, field demonstration, and validation, working with Business Development, Production, and cross-functional Engineering teams. As a key contributor to the Spectrum Sensing Team, the engineer will develop passive geolocation capabilities for next-generation distributed sensing products. Work includes TDOA, FDOA, and hybrid architectures; coherent and non-coherent localization and imaging; statistical detection and estimation; simulation and mitigation of real-world system impairments; and implementation of algorithms in software and accelerated compute environments. The role also supports hardware and systems requirements, integration, calibration, testing, field validation, and technical documentation. Periodic travel of up to 30% is required. The position must be performed on-site three to five days per week in Hawthorne, California, or Washington, D.C. Applicants must be U.S. citizens and able to obtain and maintain a U.S. security clearance. The company lists a base salary range of $110,000–$151,000, with compensation potentially varying based on experience, education, training, skills, and business considerations.

Requirements

B.S. degree in Electrical Engineering, Applied Mathematics, Physics, or a related technical field.
At least 5 years of relevant experience developing RF signal processing, estimation, or geolocation algorithms.
Strong foundation in statistical and time-domain signal processing, detection and estimation theory, backpropagation, and applied linear algebra.
Demonstrated experience developing passive RF geolocation algorithms, such as TDOA, FDOA, multilateration, direction finding, interferometry, or hybrid localization methods.
Experience applying rigorous detection frameworks such as Neyman-Pearson, Bayesian inference, CFAR, or related methods to noisy and contested RF environments.
Proficiency in Python, MATLAB, C++, or similar languages for signal-processing modeling, simulation, and implementation.
Experience evaluating algorithm performance under real-world impairments, including low SNR, synchronization error, sensor geometry uncertainty, or interference.
Ability to translate theoretical models into practical software suitable for integration with RF/SDR hardware and production code bases.
Strong verbal and written communication skills, including the ability to document technical approaches, assumptions, and performance results.
Must be able to work on-site three to five days per week in Hawthorne, California, or Washington, D.C.
Must be a U.S. citizen.
Must be able to obtain and maintain a U.S. security clearance.

Nice To Haves

M.S. or Ph.D. degree in Electrical Engineering, Applied Mathematics, Physics, or a related technical field.
Current U.S. security clearance.
Deep expertise in passive RF geolocation, interferometric processing, distributed coherent sensing, or passive imaging architectures.
Peer-reviewed contributions to geolocation techniques accepted by the relevant community.
Experience deriving estimators and analytical bounds, such as maximum likelihood estimators, Cramer-Rao lower bounds, and geolocation error budgets.
Experience optimizing signal-processing algorithms for real-time execution on GPU, FPGA, DSP, or heterogeneous compute platforms.
Experience taking algorithms from concept through simulation, integration, calibration, and field-test validation.
Ability to define system-level requirements for RF front ends, clocks, synchronization, and calibration based on algorithm sensitivity and accuracy analysis.
Expertise with software-defined radios and associated RF signal-processing toolchains.
Experience performing related technical work in military or other government environments in a fast-paced, mission-driven setting.
Experience with agile development tools such as JIRA and Confluence.

Responsibilities

Design and derive advanced passive RF geolocation algorithms, emphasizing TDOA, FDOA, and hybrid geolocation architectures across distributed sensor networks.
Develop coherent and non-coherent passive geolocation and imaging approaches, including phase-aligned multi-node processing and envelope-based localization methods.
Apply statistical signal detection frameworks, including Neyman-Pearson, Bayesian, and CFAR methodologies, to improve detection across varying noise, interference, and target conditions.
Use estimation and detection theory, maximum likelihood estimation, error-bound analysis, and linear algebra to develop robust localization solutions.
Model and mitigate real-world nonidealities, including oscillator phase noise, timing jitter, calibration error, uncertain sensor geometry, and low-SNR conditions.
Develop and refine software for passive geolocation, emitter localization, and RF scene analysis using Python, MATLAB, C++, or related languages.
Translate algorithms into efficient real-time implementations on DSP, GPU, or other accelerated compute architectures as needed.
Build high-fidelity simulation environments to evaluate geolocation accuracy, sensitivity, error budgets, and system tradeoffs.
Collaborate with hardware and systems engineers to define RF front-end and timing requirements based on their effects on end-to-end geolocation performance.
Support algorithm integration, system calibration, test planning, and field validation in representative operational environments.
Document algorithm assumptions, derivations, performance limits, and test results for internal stakeholders and external customers.
Travel periodically, up to 30%.