Senior RF Machine Learning Engineer

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.