SlipstreamJobs tracks this role from the company's public career site. Apply directly on the employer's site.
Salary: USD 180,000 - 230,000 / annual
CHAOS Industries is seeking a Staff RF Geolocation Engineer to lead the development of advanced passive RF geolocation capabilities for its electromagnetic warfare product line. This is a highly technical, hands-on role focused on deriving, implementing, and validating high-performance localization solutions that enable CHAOS's distributed systems to detect, characterize, and geolocate non-cooperative RF emitters in complex environments.
As a key contributor to the Spectrum Sensing Team, you will work across the full algorithm lifecycle—from first-principles formulation and high-fidelity modeling through software integration, calibration, field demonstration, and validation. You will collaborate closely with Business Development, Production, and cross-functional Engineering teams.
Key responsibilities include:
- 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 for interferometric performance and robust envelope-based localization methods
- Apply statistical signal detection frameworks (Neyman-Pearson, Bayesian, CFAR) to maximize probability of detection 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 operating 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
- Build high-fidelity simulation environments to evaluate geolocation accuracy, sensitivity, error budgets, and system tradeoffs
- Partner with hardware and systems engineers to define RF front-end and timing requirements by quantifying their effect on end-to-end geolocation performance
- Support algorithm integration, system calibration, test planning, and field validation in representative operational environments
- Clearly document algorithm assumptions, derivations, performance limits, and test results for internal stakeholders and external customers
- Periodic travel up to 30% required
CHAOS Industries was founded in 2022 and has raised $1 billion in funding from leading 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. The company employs 250+ people and is building next-generation distributed sensing products powered by Coherent Distributed Networks (CDN™).
REQUIREMENTS:
Minimum:
- B.S. degree in Electrical Engineering, Applied Mathematics, Physics, or related technical field
- 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 with ability to clearly document technical approaches, assumptions, and performance results
- Must be able to work on-site 3-5 days per week in Hawthorne, CA or Washington, D.C.
- U.S. citizen
- Ability to obtain and maintain a U.S. security clearance
Preferred:
- M.S. or Ph.D. degree in Electrical Engineering, Applied Mathematics, Physics, or related technical field
- Current U.S. security clearance
- Deep expertise in passive RF geolocation, interferometric processing, distributed coherent sensing, or passive imaging architectures
- Demonstrated community-accepted (peer-reviewed) contributions to geolocation techniques
- 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
