Quartermaster AI is seeking an experienced RF Signals Analyst with deep technical roots in communications and signals analysis and characterization to lead our data labeling and signal characterization efforts. You will be the primary authority on what a "signal of interest" looks like in the maritime domain, using your expertise to help define reference labeling standards and high confidence maritime RF datasets for downstream analytics and machine learning.
Signal Characterization: Use advanced analysis tools (e.g., GNURadio, inspectrum, or proprietary suites) to identify, classify, and tag diverse signals including VHF/UHF Comms, maritime radar (S-band/X-band), and AIS-dark emitters.
Data Labeling Leadership: Develop the taxonomies and ground-truth standards for our maritime RF database, ensuring labels are accurate and useful for training deep learning models.
Operational Priority Mapping: Advise the engineering team on which signals represent the highest value to our customers (e.g., illegal fishing fleets or military vessels).
AI/ML Collaboration: Work alongside DSP and ML engineers to validate model outputs, provide feedback on false positives, and identify "edge cases" in complex maritime RF environments.
Pattern Analysis: Identify and document unique signal characteristics, behaviors, and patterns for specific classes of vessels or electronic systems.
4+ years of relevant experience in RF signals analysis, SIGINT, ELINT, EW, or related signal characterization work.
Technical Proficiency: Strong technical understanding of communications and radar signal analysis, including modulation recognition, pulse and dwell behavior, bandwidth and occupancy characteristics, scan or repetition patterns, and signal behavior under noisy or interference heavy conditions.
Tooling: Experience with Linux-based signal analysis workflows and familiarity with RF data products such as IQ captures, waterfall or spectrogram views, metadata driven triage, and common SDR analysis tools.
Operational Context: Experience analyzing RF activity in maritime or similarly complex operational environments. Familiarity with maritime emitter behavior, platform associated interference, and relevant order of battle context is strongly preferred.
ML Awareness: Working understanding of how labeling quality, and taxonomy consistency affect downstream model training and evaluation.
Clearance: Active or ability to obtain and maintain a Secret clearance.
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