Senior RF Machine Learning Engineer

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.