Staff RF Geolocation Engineer

CHAOS Industries•Columbia, WA

3d•Onsite

About The Position

CHAOS Industries is seeking a proactive, detail-oriented Staff 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 help distributed systems 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 to software integration, calibration, field demonstrations, and validation, working with Business Development, Production, and cross-functional Engineering teams. As a key contributor to the Spectrum Sensing Team, the engineer will help advance spectrum sensing through high-confidence passive geolocation. The work is diverse, hands-on, and highly technical, with direct impact on next-generation distributed sensing products. The position requires periodic travel of up to 30% and on-site work 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 is a defense technology company founded in 2022, headquartered in Los Angeles, with offices in Washington, D.C., San Francisco, San Diego, Seattle, and London. It has raised $1 billion in funding and has 250 employees across five global offices. The targeted base salary range is $180,000–$230,000, excluding bonus, equity, and benefits.

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 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

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 recognized 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 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 from first principles, 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 methods, to maximize detection probability in varying noise, interference, and target conditions.
Apply 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, 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 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%.