AI Research Assistant

AI Research Assistant

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 AI Research Assistant will assist the AI/Machine Learning lead for DE Bioeffects project in their efforts to develop and refine the AI/ML backed sense making technology for the repository. They will attend project meetings at the request of the PI, co-PIs, and AI/ML lead, and may be asked to take technical notes during meetings. They will maintain a collaborative work style and help the AI/ML lead solve problems with the large interdisciplinary research team.

Physical Demands:
Sedentary 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.

Licenses/Certifications:

N/A

Education:

• Currently enrolled in a Bachelor's, Master's, or Doctoral degree program at an accredited college or university in Computer Science, Data Science, Computational Linguistics/Natural Language Processing, Information Science, Electrical and Computer Engineering, or a closely related field.

Experience:

• Demonstrated hands-on experience — through coursework, research, internship, or a personal project — building and evaluating retrieval and language-model systems, including at least one retrieval system built end to end and at least one model the candidate trained or fine-tuned themselves.

Must be able to obtain a US security clearance. If selected, 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. Final offer is contingent upon the candidate's ability to successfully obtain the necessary interim Secret security clearance, as determined by ARLIS, prior to commencing employment.

Knowledge, Skills, and Abilities:

• Proficiency in Python, including the ability to write modular, re-runnable, and documented code.
• Working familiarity with the Linux command line and with version control using Git.
• Familiarity with at least one modern machine-learning or natural-language-processing toolkit — for example, PyTorch, Hugging Face Transformers, or sentence-transformers.
• Ability to learn unfamiliar tools independently from documentation and example code, and to raise blockers promptly rather than working around them silently.
• Ability to communicate clearly in writing, to work collaboratively as part of an interdisciplinary research team, and to use standard office productivity software.

Preferences:

• Graduate-level study in Computer Science, Natural Language Processing, Machine Learning, Information Science, or a related field.
• Domain adaptation of retrieval models — fine-tuning an embedder or reranker on in-domain data; continued pretraining on a domain corpus.
• Information extraction: NER, entity linking, relation extraction — including fine-tuning extraction models on a labeled gold set.
• Knowledge graphs: building a citation/affiliation/funding graph and querying it; traversal, community detection;
  Neo4j or Network
  
  X.
• GraphRAG, or a considered view on RAG vs. GraphRAG.
• NLI / entailment or groundedness scoring.
• Constrained or schema-guided decoding.
• Multi-GPU or distributed training; experiment tracking.
• Layout-aware PDF parsing and OCR.
• REST API development (FastAPI / Flask);
  Dev Ops, CI/CD.
• Comfort working independently and collaboratively in a fast-paced, evolving environment.
• Strong organizational skills and attention to detail.
• Active or eligible for a security clearance.

Required Application Materials:
Cover Letter, Resume, List of References