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:
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Design, train, and deploy machine learning models for RF signal detection, classification, and vessel activity tracking.
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Build and maintain dataset curation pipelines, including AIS-correlated ground truth labeling, synthetic RF data generation, and augmentation strategies for class-imbalanced maritime environments.
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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.
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Develop model evaluation frameworks and benchmarking harnesses; define quantitative performance criteria and drive iterative improvement against them.
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Optimize models and inference workflows for deployment on edge compute hardware.
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Document model architecture, training methodology, dataset provenance, and validation results.
Qualifications (Preferred):
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Master's or PhD in Machine Learning, Signal Processing, or a closely related field — or equivalent demonstrated experience.
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5+ years building and deploying ML systems with a focus on RF or signals data.
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Proficiency in Python and deep learning frameworks; familiarity with RF-native tooling such as Torchsig is a strong plus.
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Strong understanding of signal alignment, temporal synchronization, and feature extraction from IQ and spectral data.
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Proven ability to ship production models, not just research prototypes.
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Experience in maritime, aerospace, or operationally demanding spectral environments.
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Experience building labeled RF datasets from ground truth sources.
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Familiarity with edge inference constraints and optimization techniques (quantization, pruning, model distillation).
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Active Secret clearance or demonstrated ability to obtain one.
Compensation Range: $210K - $260K
Originally posted on Himalayas