AI Systems Engineer (Multi Modal)

PhD or Master’s degree in Machine Learning, Computer Vision, Signal Processing, or related field. 7+ years of experience building and deploying machine learning systems, with a focus on multi-modal or sensor fusion applications. Proficiency in Python and deep learning frameworks (PyTorch or TensorFlow). Demonstrated experience working with camera imagery, RF signals and/or acoustic data. Deep understanding of signal alignment, temporal/spatial synchronization, and feature extraction across diverse data types. Proven ability to bridge research and application in production contexts. Excellent communication and collaboration skills in cross-functional, interdisciplinary teams. Experience in maritime, aerospace, or other sensor-rich environments is a plus.

Research, design, and implement advanced machine learning models for detection, classification, and tracking. Architect sensor fusion pipelines for robust perception in dynamic environments. Collaborate with domain experts to translate sensor data into model inputs. Design and oversee data pipelines for multi-modal learning. Optimize models and inference workflows for low-latency execution on edge platforms. Lead performance analysis and drive strategies for improving robustness. Prototype and operationalize novel research in sensor fusion. Contribute to architectural decisions for multi-modal AI infrastructure. Document model design, training, and validation processes.