Research, design, and implement advanced machine learning models that combine vision, RF, and acoustic signals for detection, classification, and tracking tasks.
Architect sensor fusion pipelines that support robust, redundant, and context-aware perception in dynamic environments.
Collaborate closely with domain experts and systems engineers to translate raw sensor data into actionable model inputs.
Design and oversee data pipelines for multi-modal learning, including data alignment, augmentation, and pre-processing across modalities.
Optimize models and inference workflows for low-latency execution on embedded and edge compute platforms.
Lead performance analysis across individual and fused modalities, and drive strategies for improving robustness and generalization.
Prototype and operationalize novel research in sensor fusion, uncertainty modeling, and representation learning.
Contribute to long-term architectural decisions around multi-modal AI infrastructure, tooling, and evaluation frameworks.
Document model design, training methodology, and validation processes with rigor and clarity.
Must be eligible to obtain/maintain a security clearance.

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, graph theory, and sensor fusion applications.
Proficiency in Python and deep learning frameworks such as PyTorch and Torchsig.
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.
Experience with Nvidia Jetson modules.

Machine Learning Engineer, Digital Signal Processing

Key skills