Machine Learning Application Engineer

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Job Description Summary Organization's Summary Statement: The Applied Research Laboratory for Intelligence & Security (ARLIS) at the University of Maryland is a University-Affiliated Research Center (UARC) dedicated to advancing research, innovation, and technology transition to improve decision making for U.S. national security. ARLIS combines deep scientific expertise with operational insight to address challenges in intelligence analysis, cybersecurity, artificial intelligence / machine learning, quantum science, and human-machine teaming. Researchers, scientists, engineers, and analysts at ARLIS collaborate with government agencies, industry partners, and academic institutions to deliver actionable insights and transformative solutions through research and development. Employees at ARLIS work on projects of critical importance, contribute directly to the nation's security, and are supported by a culture that values integrity, collaboration, and professional growth. The Applied Research Laboratory for Intelligence & Security (ARLIS) at the University of Maryland, College Park, is seeking qualified candidates with expertise in applied machine learning (ML) application development to build, deploy, and sustain end-to-end ML capabilities in support of U.S. national security missions. Ideal candidates will demonstrate experience translating technical problems into robust ML solutions-spanning data selection and preparation, model development, evaluation, and delivery of models into operational workflows-with a preference for candidates with experience applying natural language processing (NLP) and computer vision methods and tools. Candidates should have a foundational understanding of machine learning methods and practical familiarity with common ML libraries and frameworks (e.g., PyTorch/torch, scikit-learn, SciPy), along with experience deploying and maintaining ML systems using modern engineering practices such as CI/CD, workflow orchestration, and monitoring. Successful applicants will also be comfortable collaborating on interdisciplinary teams and communicating complex technical work through technical deliverables and briefings for government stakeholders. Successful candidates will contribute to a portfolio of government-sponsored projects addressing emerging challenges in areas such as decision support, information processing, human-machine teaming, and operational analytics, with particular emphasis on delivering ML capabilities that are reliable, testable, and maintainable. Work may include rapid prototyping as well as production-oriented engineering to transition research into usable tools, including interactive applications that enable end users to apply ML models in real-world contexts. Final appointment title and responsibilities will be based on qualifications and matched to programmatic needs. Roles and Responsibilities Roles and Responsibilities will vary by project and candidate expertise and may include: - Building end-to-end ML systems from technical problem formulation through training data selection/curation, modeling, evaluation, and delivery of ML models into usable tools and workflows, preferably with a focus on natural language programming and computer vision methods and tools - Applying core ML theory and methods in practice, including selecting appropriate approaches, implementing baselines, conducting error analysis, and iterating on model performance using common libraries and frameworks (e.g., PyTorch/torch, scikit-learn, SciPy) - Deploying and maintaining ML systems in development and operational environments, including git-based CI/CD, workflow orchestration, model packaging and Docker-based deployment, and monitoring for system health, data quality, and model performance over time - Developing interactive ML-enabled applications for end users, including front-end development using FastAPI + React (including Vite) and/or MERN/PERN stacks to deliver intuitive user experiences powered by ML models - Supporting proof-of-concept back-end development and requirements generation, including prototyping data stores and retrieval patterns and translating findings into clear requirements for a back-end engineering team (e.g., using Postgres (including pgvector), MongoDB, and/or ElasticSearch) - Collaborating with interdisciplinary teams including researchers, engineers, domain experts, and analysts to integrate ML capabilities into broader systems and mission workflows - Preparing technical deliverables and briefings for government stakeholders, translating complex technical work into clear, decision-relevant products (e.g., reports, slide briefings, memos, and research summaries) - Contributing to research proposals and technical work plans, including scoping technical approaches, estimating effort, identifying risks, and supporting sponsor engagement as needed Must be able to obtain a U.S. security clearance. If selected, you must meet the requirements for access to classified

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