Assistant Research Engineer; Artificial Intelligence​/Machine Learning - Autonomous Systems

Organization's Summary Statement:
The University of Maryland (UMD) Maryland Autonomous Technologies Research Innovation and eXploration Lab (MATRIX Lab) is an autonomous systems research, development, and education center operated by the

A. James Clark School of Engineering at the University of Maryland, located at the University System of Maryland at Southern Maryland (USMSM) in St. Mary's County. The lab conducts cutting-edge research in autonomous technologies in partnership with federal sponsors, including the U.S. Navy, Office of Naval Research (ONR), Naval Air Warfare Center Aircraft Division (NAWCAD), as well as leading defense industry partners.

The Clark School of Engineering at the University of Maryland serves as a catalyst for high-quality research, innovation, and learning, preparing students to address 21st-century grand challenges in energy, environment, security, and human health. The Clark School is dedicated to transforming the engineering discipline, accelerating entrepreneurship, and converting research and learning into innovations that benefit society.

The Assistant Research Engineer (Artificial Intelligence / Machine Learning - Autonomous Systems) conducts applied and translational research at the intersection of machine learning, multi-modal sensing, and autonomous systems at the MATRIX Lab. Reporting to Lab leadership and working in close collaboration with University of Maryland faculty, this position leads and supports the development of AI/ML-enabled capabilities across three primary mission domains: aerial autonomy (including GPS-denied uncrewed aerial system operations and maritime integration), underwater autonomy, and multi-modal sensing and perception.

The position is a full-time research appointment, suited for a highly motivated researcher with a strong background in applied machine learning and autonomous systems who seeks to conduct impactful, defense-relevant research in a fast-paced lab environment. Please note this position is located at the MATRIX Lab facilities in California, MD.

• Developing end-to-end AI/ML systems for autonomous platforms, from technical problem formulation through training data selection and curation, model design and training, evaluation, and delivery of models into usable tools, workflows, and onboard vehicle capabilities, with a focus on computer vision, multi-modal perception, and reinforcement learning methods.
• Applying core ML theory and methods in practice for autonomous systems applications, including selecting appropriate modeling approaches, implementing baselines, conducting error analysis, and iterating on model performance using common libraries and frameworks (e.g., PyTorch, scikit-learn, ROS/ROS2).
• Designing and implementing adaptive and uncertainty-aware learning methods, such as knowledge distillation, contextual learning, Gaussian Process-based active context selection, and policy optimization under uncertainty, to enable robust autonomous behavior across variable and degraded operational conditions.
• Developing and evaluating multi-modal sensing and sensor fusion architectures integrating RGB cameras, LiDAR, IMUs, acoustic sensors, etc. to support perception and decision-making for aerial, maritime, and underwater autonomous systems.
• Designing, building, and executing controlled real-world experiments using MATRIX Lab testbed facilities (including the Omni-domain Autonomous Systems Integration Space (OASIS) and Hydrodynamics Lab) and developing quantitative evaluation pipelines to benchmark autonomous system performance across disturbance conditions and operational scenarios.
• Deploying and maintaining AI/ML systems in development and experimental environments, including version-controlled codebases, model packaging, simulation-to-real transfer workflows, and integration with onboard autonomy stacks on UAS, maritime, and underwater platforms.
• Developing interactive ML-enabled tools and decision-support applications for researchers and government end users, including back-end and front-end components as appropriate (e.g., FastAPI, React, or equivalent frameworks) to surface autonomous system outputs in usable, operator-facing formats.
• Collaborating with interdisciplinary teams including UMD faculty, graduate and undergraduate students, defense industry partners, and Navy program stakeholders to integrate AI/ML capabilities into broader autonomous systems and mission workflows.
• Preparing…