About the job
About Us:
At Quartermaster AI, we are committed to ensuring that the ocean is a safe and sustainably managed resource for everyone. By harnessing the power of advanced AI and robotics, we unlock capabilities that were previously unimaginable. Our innovative distributed open-ocean systems empower every vessel to sense, compute, and communicate, significantly enhancing maritime domain awareness for those who require it most.
Job Description:
We are seeking a talented RF and AI Systems Engineer specializing in Artificial Intelligence with a focus on RF analysis. In this pivotal role, you will develop and deploy machine learning systems that utilize Software Defined Radio (SDR) data for real-time maritime intelligence. You will collaborate with our team to construct AI models that provide contextual insights into vessel activities based on observed RF signatures. This position is perfect for individuals who excel in navigating complex and sometimes ambiguous challenges, bridging theory with practical implementation, and are motivated by the prospect of creating AI systems that operate in dynamic, constrained, and remote environments.
Key Responsibilities:
Conduct research, design, and implement cutting-edge machine learning models that integrate vision, RF, and acoustic signals for detection, classification, and tracking tasks.
Architect sensor fusion pipelines that enable robust, redundant, and context-aware perception in dynamic settings.
Work closely with domain experts and systems engineers to transform raw sensor data into actionable inputs for models.
Design and manage data pipelines for multi-modal learning, including data alignment, augmentation, and preprocessing across various modalities. Optimize models and inference workflows for low-latency execution on embedded and edge computing platforms.
Lead performance analysis of individual and fused modalities, driving strategies for enhancing robustness and generalization.
Prototype and operationalize innovative research in sensor fusion, uncertainty modeling, and representation learning. Contribute to long-term architectural decisions regarding multi-modal AI infrastructure, tooling, and evaluation frameworks.
Thoroughly document model design, training methodologies, and validation processes with precision and clarity.
