Staff RF Geolocation Engineer

CHAOS Industries; Washington (District of Columbia); On-site; USD 180,000 - 230,000; Full time

Benefits offered by this job

Health Benefits

401k with company match

FSA

HSA

Life insurance

Free daily lunch

No meeting Fridays

Unlimited PTO

Job summary

CHAOS Industries is seeking a Staff RF Geolocation Engineer to lead the development of advanced passive RF geolocation capabilities for our electromagnetic warfare products. You will derive, implement, and validate high-performance localization solutions across distributed sensor networks, collaborating with cross-functional teams to ensure robust, real-time performance.

The role covers algorithm lifecycle from first-principles formulation to software integration, calibration, field

Qualifications

• BS degree in a technical field and 5+ years in RF signal processing, estimation, or geolocation.
• Strong foundation in detection theory, estimation, and linear algebra.
• Experience with passive RF geolocation methods such as TDOA, FDOA, multilateration, interferometry, or hybrid localization.
• Proficiency in Python, MATLAB, C++, or similar languages for modeling and implementation.
• Ability to translate models into production-ready software for RF/SDR hardware.
• On-site work 3–5 days per week in Hawthorne, CA or Washington, D.C. and ability to obtain security clearance.

Responsibilities

• Design and derive advanced passive RF geolocation algorithms from first principles for distributed sensor networks.
• Develop coherent and non-coherent passive geolocation and imaging approaches with multi-node processing.
• Apply Neyman-Pearson, Bayesian, CFAR frameworks to maximize detection probability under varied conditions.
• Apply maximum likelihood estimation and linear algebra to formulate robust localization solutions.
• Model real-world nonidealities such as oscillator phase noise and calibration error.
• Develop and refine software for passive geolocation and RF scene analysis using Python, MATLAB, C++.
• Translate algorithms into real-time implementations on DSP, GPU, or accelerated architectures.
• Build high-fidelity simulations to evaluate accuracy, sensitivity, and tradeoffs.
• Collaborate with hardware and systems engineers to define RF front-end and timing needs.
• Document assumptions, performance, and test results for stakeholders and customers.

Skills

Passive RF geolocation

TDOA/FDOA

Statistical signal processing

Estimation theory

Python/MATLAB/C++

Documentation of approaches

Travel up to 30%

Education

BS in Electrical Engineering, Applied Mathematics, Physics, or related field

MS/PhD in Electrical Engineering, Applied Mathematics, Physics, or related field

Tools

Python

MATLAB

C++

GPU/DSP platforms

Job description

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™), empowering warfighters, commercial air operators, and border protection teams to act faster, adapt rapidly, and stay ahead of evolving threats.

CHAOS Industries was founded in 2022 and has raised a total of $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. For more information, please visit www.chaosinc.com .

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. This role is 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. The engineer will contribute across the full algorithm lifecycle, from first-principles formulation and high-fidelity modeling through software integration, calibration, field demonstration, and validation, and will collaborate closely with Business Development, Production, and cross-functional Engineering teams.

Responsibilities:

As a key contributor to the Spectrum Sensing Team, the Staff RF Geolocation Engineer will help redefine our spectrum sensing capabilities around high-confidence passive geolocation. The day-to-day will be diverse, hands‑on, and highly technical, with direct impact on next‑generation distributed sensing products. The engineer will:

• 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 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 and analytically defensible 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 as system needs require
• 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 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
• This role will require periodic travel, up to 30%.
• B.S. degree in Electrical Engineering, Applied Mathematics, Physics, or a 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 the 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.
• Ability to obtain and maintain a U.S. security clearance

Preferred Requirements:

• 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
• 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

Why CHAOS?

• Health Benefits: Medical, dental, and vision benefits 100% paid for by the company
• Additional benefits: 401k (+ 50% company match up to 6% of pay), FSA, HSA, life insurance, and more
• Our Perks: Free daily lunch, ‘No meeting Fridays’, unlimited PTO, casual dress code
• Compensation Components: Competitive base salaries, generous pre‑IPO stock option grants, relocation assistance, and (coming soon!) annual bonuses
• Team Growth: 250 employees and counting across 5 global offices

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 (but not limited to) work experience, education and/or training, critical skills, and/or business considerations.

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Export Controls - This position may require access to information and technology that is subject to U.S. export controls. Your responses to the questions below will be used solely to determine your eligibility under U.S. law to receive information and materials subject to U.S. export controls.

Voluntary Self-Identification

For government reporting purposes, we ask candidates to respond to the below self-identification survey. Completion of the form is entirely voluntary. Whatever your decision, it will not be considered in the hiring process or thereafter. Any information that you do provide will be recorded and maintained in a confidential file.

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.

If you believe you belong to any of the categories of protected veterans listed below, please indicate by making the appropriate selection. As a government contractor subject to the Vietnam Era Veterans Readjustment Assistance Act (VEVRAA), we request this information in order to measure the effectiveness of the outreach and positive recruitment efforts we undertake pursuant to VEVRAA. Classification of protected categories is as follows:

A "disabled veteran" is one of the following: a veteran of the U.S. military, ground, naval or air service who is entitled to compensation (or who but for the receipt of military retired pay would be entitled to compensation) under laws administered by the Secretary of Veterans Affairs; or a person who was discharged or released from active duty because of a service‑connected disability.

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An "active duty wartime or campaign badge veteran" means a veteran who served on active duty in the U.S. military, ground, naval or air service during a war, or in a campaign or expedition for which a campaign badge has been authorized under the laws administered by the Department of Defense.

An "Armed forces service medal veteran" means a veteran who, while serving on active duty in the U.S. military