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Senior RF Machine Learning Engineer

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COMPANYQuartermaster

Quartermaster AI is seeking a Senior AI/ML Engineer with an emphasis in RF analysis to develop and deploy machine learning systems that utilize RF data for real-time maritime intelligence. You’ll work in a small team of experienced engineers to build detection, classification, and tagging models that help provide contextual understanding of vessel activity based on observed RF signatures.

Key Responsibilities:

  • Design, train, and deploy machine learning models for RF signal detection, classification, and vessel activity tracking.

  • Build and maintain dataset curation pipelines, including AIS-correlated ground truth labeling, synthetic RF data generation, and augmentation strategies for class-imbalanced maritime environments.

  • Build the interface between DSP feature outputs and model inputs by defining pre-processing, normalization, and feature extraction requirements in coordination with the DSP engineer.

  • Develop model evaluation frameworks and benchmarking harnesses; define quantitative performance criteria and drive iterative improvement against them.

  • Optimize models and inference workflows for deployment on edge compute hardware.

  • Document model architecture, training methodology, dataset provenance, and validation results.

Qualifications (Preferred):

  • Master's or PhD in Machine Learning, Signal Processing, or a closely related field — or equivalent demonstrated experience.

  • 5+ years building and deploying ML systems with a focus on RF or signals data.

  • Proficiency in Python and deep learning frameworks; familiarity with RF-native tooling such as Torchsig is a strong plus.

  • Strong understanding of signal alignment, temporal synchronization, and feature extraction from IQ and spectral data.

  • Proven ability to ship production models, not just research prototypes.

  • Experience in maritime, aerospace, or operationally demanding spectral environments.

  • Experience building labeled RF datasets from ground truth sources.

  • Familiarity with edge inference constraints and optimization techniques (quantization, pruning, model distillation).

  • Active Secret clearance or demonstrated ability to obtain one.