Quartermaster is a maritime sensing startup that builds SmartMast, a mast-mounted package of weather-hardened sensors and an analytics platform for continuous distributed maritime monitoring.
ABOUT US: 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. JOB DESCRIPTION: 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.