• Lead the design, training, and deployment of computer vision and perception models for tasks such as vessel detection, classification, and tracking. • Architect and optimize inference pipelines for real-time performance on edge devices, including GPU-accelerated and mixed-precision inference. • Implement and refine algorithms for scene understanding, object segmentation, and environmental awareness using both image and sensor fusion data. • Collaborate with hardware and software teams to ensure model compatibility with embedded systems and available compute resources. • Develop techniques for edge-case detection, dataset curation, and self-healing model behavior in dynamic, non-ideal environments. • Apply active learning, domain adaptation, and synthetic data strategies to improve model robustness and generalization. • Work closely with RF and sensor teams to integrate complementary signal data into multimodal classification and detection pipelines. • Own model evaluation processes and benchmarking across hardware platforms and field conditions. • Maintain best practices for model versioning, traceability, and validation in safety-critical applications.