Staff RF Geolocation Engineer

Washington, District of Columbia, United States CHAOS Industries is redefining modern defense with a multi‑product portfolio that gives the ultimate advantage—domain dominance. The company's products are powered by Coherent Distributed Networks (CDN™), enabling warfighters, commercial air operators, and border protection teams to act faster, adapt rapidly, and stay ahead of evolving threats. CHAOS was founded in 2022 and has raised $1 billion in funding. The company is headquartered in Los Angeles, with offices in Washington, D.C., San Francisco, San Diego, Seattle, and London. Role Overview We are seeking a proactive and detail‑oriented Staff RF Geolocation Engineer to lead the development of advanced passive RF geolocation capabilities for our electromagnetic warfare product line. The engineer will derive, implement, and validate high‑performance localization solutions that enable CHAOS’s distributed systems to detect, characterize, and geolocate non‑cooperative RF emitters in complex environments. Responsibilities Design and derive advanced passive RF geolocation algorithms from first principles, emphasizing TDOA, FDOA, and hybrid 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 (Neyman‑Pearson, Bayesian, CFAR) to maximize probability of detection in variable noise and interference. Apply estimation and detection theory (maximum likelihood, error‑bound analysis, linear algebra) to formulate robust solutions. Model, simulate, and mitigate real‑world nonidealities such as phase noise, jitter, calibration error, sensor geometry uncertainty, and low‑SNR conditions. Develop, implement, and refine software for passive geolocation using Python, MATLAB, C++, or related languages. Translate mathematical algorithms into efficient real‑time implementations on DSP, GPU, or other accelerated architectures. Build high‑fidelity simulation environments to evaluate accuracy, sensitivity, error budgets, and system trade‑offs before deployment. Partner with hardware and systems engineers to define RF front‑end and timing requirements. Support algorithm integration, calibration, test planning, and field validation in representative operational environments. Document algorithm assumptions, derivations, performance limits, and test results for internal and external stakeholders. Travel up to 30% as required. Qualifications B.S. degree in Electrical Engineering, Applied Mathematics, Physics, or a related technical field. 5+ years of experience developing RF signal processing, estimation, or geolocation algorithms. Strong foundation in statistical and time‑domain signal processing, detection and estimation theory, and linear algebra. Demonstrated experience with passive RF geolocation methods such as TDOA, FDOA, multilateration, direct‑finding, interferometry, or hybrid localization. Experience applying rigorous detection frameworks (Neyman-Pearson, Bayesian inference, CFAR) in contested RF environments. Proficiency in Python, MATLAB, C++, or similar languages for modeling and implementation. Experience evaluating performance under real‑world impairments (low SNR, synchronization error, sensor geometry uncertainty). Ability to translate theoretical models into practical software for integration with RF/SDR hardware and production codebases. Strong verbal and written communication skills, with clear documentation of technical approaches. Must be able to work on‑site 3–5 days per week in Hawthorne, CA or Washington, D.C. Ability to obtain and maintain a U.S. security clearance. Preferred Requirements M.S. or Ph.D. in Electrical Engineering, Applied Mathematics, Physics, or a related 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. Experience deriving estimators, Cramer‑Rao lower bounds, and error budgets. Experience optimizing algorithms for real‑time execution on GPU, FPGA, DSP, or heterogeneous 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. Expertise with software‑defined radios and associated RF signal‑processing toolchains. Experience in military or government environments performing related work in a fast‑paced, mission‑driven setting. Experience with agile tools such as JIRA and Confluence. Salary Range Salary Range: $180,000 - $230,000 The stated compensation range reflects only the targeted base compensation range and excludes additional earnings such as bonus, equity, and benefits. If your compensation requirements fall outside of the range, we still encourage you to apply. The salary range for this role is an estimate based on a range of compensation factors, inclusive of base salary only. Actual salary offer may vary based on work experience, education, and other factors. Benefits Medical, dental, and vision benefits 100% paid by the company. 401(k) with 50% company match up to 6% of pay, FSA, HSA, life insurance, and more. Free daily lunch, “No meeting Fridays,” unlimited PTO, casual dress code. Competitive base salaries, pre‑IPO stock option grants, relocation assistance, and annual bonuses (coming soon). Team growth of 250 employees across 5 global offices. Equal Employment Opportunity Statement As set forth in CHAOS Industries’s Equal Employment Opportunity policy, we do not discriminate on the basis of any protected group status under any applicable law. #J-18808-Ljbffr CHAOS Industries