Quartermaster - Senior RF Machine Learning Engineer
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Requirements
• 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.
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.
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