Senior RF Geolocation Engineer

CHAOS Industries•Washington, DC

11d•$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 focuses on deriving, implementing, and validating high-performance localization solutions that enable distributed systems to detect, characterize, and geolocate non-cooperative RF emitters in complex environments. The engineer will contribute throughout the algorithm lifecycle, from first-principles formulation and high-fidelity modeling through software integration, calibration, field demonstrations, and validation. The position works closely with Business Development, Production, and cross-functional Engineering teams as part of the Spectrum Sensing Team. Periodic travel of up to 30% is required. The engineer will develop passive geolocation and imaging algorithms, evaluate their performance under realistic operating conditions, translate mathematical approaches into practical software, and support integration and field validation. The role requires working on-site 3–5 days per week in Hawthorne, California, or Washington, D.C. Candidates must be U.S. citizens and able to obtain and maintain a U.S. security clearance. CHAOS Industries was founded in 2022 and develops defense products powered by Coherent Distributed Networks (CDN™). The company is headquartered in Los Angeles and has offices in Washington, D.C., San Francisco, San Diego, Seattle, and London. The job posting states a workforce of 250 employees across five global offices. The base salary range is $110,000–$151,000; actual offers may vary based on factors including experience, education, training, skills, and business considerations. Additional compensation may include bonuses, equity, and benefits.

Requirements

Bachelor’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 modeling, simulation, and implementation of signal processing algorithms.
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 clearly.
Must be able to work on-site 3–5 days per week in Hawthorne, CA, or Washington, D.C.
Must be a U.S. citizen.
Ability to obtain and maintain a U.S. security clearance.

Nice To Haves

Master’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 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 in military or other government environments performing related technical work 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 from first principles, with emphasis on 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 for interferometric performance and robust envelope-based localization methods.
Apply statistical signal detection frameworks, including Neyman-Pearson, Bayesian, and CFAR methodologies, to maximize detection probability across varying noise, interference, and target conditions.
Apply estimation and detection theory, including maximum likelihood estimation, error-bound analysis, and linear algebraic methods, to formulate robust localization solutions.
Model, simulate, and mitigate real-world nonidealities such as oscillator phase noise, timing jitter, calibration error, uncertain sensor geometry, and low-SNR conditions.
Develop, implement, and refine software for passive geolocation, emitter localization, and RF scene analysis using Python, MATLAB, C++, or related languages.
Translate mathematically intensive 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 before deployment.
Partner with hardware and systems engineers to define RF front-end and timing requirements by quantifying 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%.