Quartermaster AI

RF Signals and Data Analyst

Arlington, VA

No sponsorshipDetected 110 days ago

PythonLinuxMachine LearningRoboticsSignal ProcessingCommunicationCollaboration

About the role

This role focuses on turning real world RF sensor data into structured ground truth for machine learning.
You will analyze maritime RF events using spectrograms, waterfall plots, PSDs, metadata, and contextual sources like AIS and camera data when available.
You will help define signals of interest, identify interference and host-platform noise, and label signals consistently for model development.

Help define and maintain a scalable maritime RF labeling taxonomy, including signal classes, confidence levels, rejection categories, and ambiguity handling.
Identify and document recurring host vessel interference, platform artifacts, and environmental noise to support rejection library development.
Collaborate with DSP and ML engineers to review false positives, false negatives, and edge cases, and improve labeling standards over time.
Use available contextual data such as AIS, camera imagery, collection metadata, and sensor state to support signal interpretation when appropriate.

3+ years of experience in one or more of the following: RF signal analysis, SDR-based signal review, EW/SIGINT/ELINT analysis, RF dataset creation, or technical signal characterization.
Experience working with structured labeling, annotation, classification, or technical review workflows where consistency and traceability matter.
Comfort working in a Linux-based environment using Python, SDR tools, notebooks, or other RF analysis environments to inspect, organize, and process signal data.
Ability to communicate clearly with engineers and translate signal observations into actionable labeling guidance.
Experience in maritime RF environments or other cluttered, interference heavy operational environments.
Practical experience working with RF data products such as IQ captures, spectrograms, waterfall plots, PSDs, or other time frequency representations.
Understanding of how label quality, taxonomy design, multi-sensor context (for example AIS, EO/IR, or geolocation), and rejection categories affect downstream ML training and evaluation.
Active clearance or ability to obtain and maintain a Secret clearance.

Build and refine high quality labeled datasets for machine learning, ensuring labels are technically defensible, consistent, and auditable.

At Quartermaster AI, we believe the ocean should be a safe and sustainably managed resource for all.
By leveraging cutting-edge AI and robotics, we unlock capabilities that were only recently impossible.
Our distributed open-ocean systems enable every vessel to sense, compute, and communicate, enhancing maritime domain awareness for those who need it most.

This listing is sourced directly from Quartermaster AI's careers page and normalized into a canonical job model.