Multi Modal AI Systems Engineer

PhD or Master’s degree in Machine Learning, Computer Vision, Signal Processing, or a closely 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 such as 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—delivering high-performance models in production contexts Excellent communication and collaboration skills in cross-functional, interdisciplinary teams Experience in maritime, aerospace, or other sensor-rich environments is a significant plus

Research, design, and implement advanced machine learning models combining vision, RF, and acoustic signals. Architect sensor fusion pipelines for robust perception. Collaborate with domain experts to translate raw sensor data into actionable model inputs. Design and oversee data pipelines for multi-modal learning. Optimize models and inference workflows for low-latency execution on embedded and edge compute platforms. Lead performance analysis across modalities and drive improvement strategies. Prototype and operationalize novel research in sensor fusion and uncertainty modeling. Contribute to long-term architectural decisions for multi-modal AI infrastructure. Document model design, training methodology, and validation processes.