{"id":42164,"url":"https://alion.io/job/umd-machine-learning-application-engineer","title":"Machine Learning Application Engineer","company":{"id":3379,"name":"University of Maryland","domain":"umd.edu","url":"https://alion.io/company/umd","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"B","score":78,"open_postings":32,"ghost_share":0,"stale_share":0.875,"repost_share":0,"time_to_fill_p50_days":44,"computed_at":"2026-10-17T05:46:00Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"staff","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["College Park, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":49072,"max":252288,"currency":"USD","period":"year","gross":null,"usd_annual":49072},"salary_estimate":null,"experience_years_min":10,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"CI/CD","optional":false},{"name":"Computer Vision","optional":false},{"name":"Docker","optional":false},{"name":"ElasticSearch","optional":false},{"name":"FastAPI","optional":false},{"name":"Machine Learning","optional":false},{"name":"NLP","optional":false},{"name":"pgvector","optional":false},{"name":"PostgreSQL","optional":false},{"name":"PyTorch","optional":false},{"name":"Scikit-learn","optional":false},{"name":"SciPy","optional":false},{"name":"Python","optional":true}],"status":"live","first_seen_at":"2026-06-11T00:00:00Z","employer_posted_date":"2026-06-11","last_verified_at":"2026-10-17T04:59:25Z","board_verified":true,"closed_at":null,"days_open":129,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":129},"description":"Job Description Summary\nOrganization'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.\nThe 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.\nCandidates 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.\nSuccessful 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.\nRoles and Responsibilities\nRoles and Responsibilities will vary by project and candidate expertise and may include:\nBuilding 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\nApplying 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)\nDeploying 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\nDeveloping 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\nSupporting 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)\nCollaborating with interdisciplinary teams including researchers, engineers, domain experts, and analysts to integrate ML capabilities into broader systems and mission workflows\nPreparing 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)\nContributing to research proposals and technical work plans, including scoping technical approaches, estimating effort, identifying risks, and supporting sponsor engagement as needed\nMust be able to obtain a U.S. security clearance. If selected, you must meet the requirements for access to classified information and will be subject to a government security clearance investigation that includes criminal and credit history checks, as well as verification of U.S. citizenship, birth, education, employment, and military history.\nFinal offer is contingent upon the candidate’s ability to successfully obtain the necessary interim Secret security clearance, as determined by the U.S. Government, prior to commencing employment.\nQualifications\nARLIS is hiring at all levels in two complementary tracks: Research Specialist and Research Engineer.\nResearch Specialist Qualifications:\nEntry Level:\nBachelor’s degree in a relevant field such as Computer Science, Computer Engineering, Software Engineering, Data Science, or related discipline, plus relevant experience.\n~2 years of demonstrated experience in organizing and managing projects through volunteer, extracurricular, or work activities\nDemonstrated potential for excellence in the execution, administration, and/or management of research or academic programs\nAbility to work as part of a multi-disciplinary opportunity team in a matrixed organization with a collaborative and team-centric culture\nStrong oral and written communication skills\nExcellent analytical and deep-dive skills\nMid-Level:\nMaster’s degree in a relevant field such as Computer Science, Computer Engineering, Software Engineering, Data Science, or related discipline, plus relevant experience and 3+ years of experience in a technical role or project management position for defense or intelligence R&D programs with experience in technology assessment, systems design, system analysis, executing on multiple technical programs with scheduled deliverables, and communicating and interfacing with government customers; or Bachelor’s Degree and 5+ years of relevant experience in a role of equivalent responsibility\nSuperior ability to deliver excellence in the execution, administration, and/or management of research or academic programs\nInteract with U.S. government sponsors, customers, and stakeholders\nAbility to work as part of a multi-disciplinary opportunity capture team in a matrixed organization with a collaborative and team-centric culture\nStrong oral and written communication skills\nExcellent analytical and deep-dive skills\nSenior-Level:\nMaster’s degree in a relevant field such as Computer Science, Computer Engineering, Software Engineering, Data Science, or related discipline, plus relevant experience and 7+ years of experience in a technical role or project management position for defense or intelligence R&D programs with experience in technology assessment, systems design, system analysis, executing on multiple technical programs with scheduled deliverables, and communicating and interfacing with government customers; or Bachelor’s Degree and 10+ years of relevant experience in a role of equivalent responsibility\nSuperior ability to deliver excellence in the execution, administration, and/or management of research or academic programs\nKnowledge of DoD appropriations and requirements processes\nLead engagements with U.S. government sponsors, customers, and stakeholders\nAbility to work as part of a multi-disciplinary opportunity capture team in a matrixed organization with a collaborative and team-centric culture\nStrong oral and written communication skills\nExcellent analytical and deep-dive skills\nResearch Engineer Qualifications:\nAssistant Research Engineer:\nPhD plus 5 years of research experience in a relevant field such as Computer Science, Computer Engineering, Software Engineering, Data Science, or related discipline; including success leading research projects and a record of engineering achievement and show promise of continued productivity\nAbility to:\nInitiate new projects with long-term research goals, tackling more complex problems than an Assistant Research Engineer, leading work on multiple, concurrent projects, and managing cross-functional engineering teams\nServe as Principal Investigator or having primary responsibility for a significant part of a project\nInteract with U.S. government sponsors, customers, and stakeholders\nExpected to identify funding opportunities and serve as Principal Investigator on large proposals\nMentor junior staff and Professional Track Faculty Specialists and participating in their promotion sub-committees\nAssociate Research Engineer:\nPhD plus 5 years of PhD-level research experience, including success capturing and leading large projects and a record of significant engineering achievement and show promise of continued productivity\nRecognized as an authoritative subject matter expert and receive commensurate recognition\nAbility to:\nInitiate new projects with long-term research goals, tackling more complex problems than an Assistant Research Engineer, leading work on multiple, concurrent projects, and managing cross-functional engineering teams\nServe as Principal Investigator or having primary responsibility for a significant part of a project\nInteract with U.S. government sponsors, customers, and stakeholders\nIdentify funding opportunities and serve as Principal Investigator on large proposals\nMentor junior staff and Professional Track Faculty Specialists and participating in their promotion sub-committees\nFull Research Engineer:\nPh.D. with at least 10 years of PhD-level research experience, including success capturing and leading large projects and a record of significant engineering achievement and show promise of continued productivity\nEstablished reputation for outstanding engineering practice, design, and development and viewed nationally as an authoritative subject matter expert\nProven Ability to:\nTackle problems of considerable scope and complexity with currently non-existent solutions requiring unconventional, novel approaches and sophisticated research techniques.\nPursue funding opportunities and serve as Principal Investigator or have primary responsibility for a significant part of a multi-investigator project\nLead the design and implementation of large projects with many components (subcontractors, teams, etc.).\nLead engagements with U.S. government sponsors, customers, and stakeholders\nBuild and execute new research programs\nHold leadership positions within ARLIS, on campus, or professional committees\nMentor junior staff and Professional Track Faculty Specialists and participating in their promotion sub-committees\nPhysical Demands:\nSedentary work performed in a normal office environment; exerts up to 10 pounds of force occasionally and/or negligible amount of force frequently or constantly to lift, carry, push, pull or otherwise move objects, including the human body. Ability to attend meetings both on and off campus. Spending long hours in front of a computer screen.\nPreferences:\nActive TS/SCI\nLicenses/ Certifications: N/A\nMinimum Qualifications\nThe appointee shall hold a Bachelor’s degree in a relevant area and show potential for excellence in the administration and/or management of academic or research programs. 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