Senior Rf ML Engineer

### **About Us:** At [Quartermaster](https://himalayas.app/companies/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](https://himalayas.app/companies/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. ### Compensation Range: $210K - $260K Originally posted on [Himalayas](https://himalayas.app)