Assistant Research Engineer (Artificial Intelligence / Machine Learning - Autonomous Systems)

University of Maryland•College Park, MD

4d•$90,000 - $115,000•Onsite

About The Position

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.

Requirements

M.S. in Computer Science, Electrical Engineering, Aerospace Engineering, Mechanical Engineering, Robotics, or a closely related field.
Demonstrated record of peer-reviewed publication is required.
Hands-on experience with physical autonomous vehicle platforms (aerial, ground, or marine) is required.
Ability to work independently and collaboratively within a multidisciplinary research team operating across concurrent sponsored projects.
Strong written and oral communication skills, including demonstrated ability to author technical reports, conference papers, and journal articles.
Ability to manage and prioritize research tasks across multiple active projects with competing deadlines.
Commitment to responsible research conduct and sensitivity to a culturally and ethnically diverse academic and research community.
Must be eligible to work on U.S. government-sponsored defense research; U.S. citizenship or permanent residency preferred.

Nice To Haves

Advanced coursework, research experience, or demonstrated self-directed learning across multiple subfields of AI/ML, including areas such as computer vision, natural language processing, probabilistic modeling, or control theory, reflecting breadth beyond a single technical specialty.
Familiarity with the theoretical foundations underlying commonly used methods, such as information-theoretic principles, optimization landscapes, or statistical learning theory, enabling principled selection and adaptation of approaches across novel problem settings.
Experience working at the intersection of software and hardware, including debugging integration issues between onboard compute, sensors, and autonomy stacks on physical platforms.
Exposure to safety-critical or real-time system constraints, such as latency requirements, fault tolerance, or graceful degradation under sensor failure, and how these shape algorithm and architecture choices.
Comfort working across the full development stack, from exploratory research and prototyping through production-quality implementation, testing, and handoff.
Experience with collaborative software development practices including git-based workflows, code review, documentation, and reproducible experiment management.
Familiarity with containerized and cloud or HPC computing environments for large-scale model training and evaluation.
Track record of translating research outputs into tangible artifacts (e.g., open-source tooling, technical reports, demonstration systems, or transition-ready prototypes) beyond peer-reviewed publication alone.
Experience briefing technical work to non-technical or mixed audiences, including government program managers, operational end users, or senior leadership.
Demonstrated ability to scope and self-manage research tasks with limited supervision, including identifying when to go deep versus when to move on and seek collaborator input.
General familiarity with the federal defense research and acquisition ecosystem, including an understanding of how university-based applied research connects to sponsor mission needs, transition pathways, and program objectives.
Awareness of relevant ethical, legal, and policy considerations surrounding autonomous systems in defense contexts, including questions of human-machine teaming, rules of engagement compliance, and responsible AI principles.
A Ph.D. in Computer Science, Electrical Engineering, Aerospace Engineering, Mechanical Engineering, Robotics, or a closely related field is preferred.

Responsibilities

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 technical deliverables and briefings for government and industry stakeholders, translating complex research findings into clear, decision-relevant products such as reports, slide briefings, demonstration summaries, and white papers.
Leading technical proposal writing and contributing to sponsored research proposals and technical work plans, including scoping technical approaches, estimating level of effort, identifying risks, and supporting engagement with federal sponsors such as ONR, NAWCAD, Air Force Research Laboratory, etc.
Mentoring graduate and undergraduate student researchers, providing technical guidance on AI/ML methods, experimental design, and research communication.