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Vanderbilt University · 3 weeks ago

Senior Artificial Intelligence Research Engineer

Nashville, TN

Full-time

Onsite

Mid, Senior Level

3+ years exp

Vanderbilt University is seeking a Senior Artificial Intelligence Research Engineer to contribute to the Wicked Problems Laboratory focused on advancing research in agentic AI frameworks and AI-driven analytic tools for national security applications. The role involves leading AI research activities, conducting threat analysis, and collaborating with interdisciplinary teams to enhance cyber resiliency and technology adoption.

E-LearningEducationHigher Education

No H1B

U.S. Citizen Only

Responsibilities

Lead and support AI research activities, including training and tuning small- to medium-sized large language models and coordinating multi-agent AI systems to advance research objectives

Conduct AI-driven threat analysis to produce actionable security insights

Support the integration, evaluation, and testing of AI capabilities in laboratory and partner operational environments

Develop high-quality documentation—including system diagrams, test results, and sponsor deliverables—to support technology maturation and transition

Maintain expertise in emerging AI-driven threats, adversarial models, and advanced AI technologies to shape research direction

Collaborate with faculty, interdisciplinary teams, and external partners to support grant objectives, publications, prototypes, and demonstrations

Present technical insights through briefings, reports, and presentations to academic, industry, and operational audiences

Support strategic relationships with government and industry partners while safeguarding research data, systems, and intellectual property

Qualification

AI engineeringLarge language modelsPythonSecure system designData pipelinesAdversarial AIMulti-agent systemsExperiment designTechnical communicationDocumentation

Required

Bachelor's degree in Computer Science, Computer Engineering, Data Science, or a related technical field is required

3–5+ years of hands-on AI engineering experience in enterprise, research, or critical-infrastructure environments

Direct experience training and tuning foundation models, including parameter-efficient techniques (LoRA, QLoRA, adapters), dataset construction, and evaluation across benchmarks

Practical expertise integrating LLMs with external tools and APIs, including retrieval systems, vector databases, function calling, or multi-agent orchestration

Expert-level Python proficiency, including designing and optimizing complex AI research pipelines, building high-performance training and inference systems, and developing secure, production-quality tooling in modern Python ecosystems (PyTorch, Hugging Face, Ray, vLLM)

Strong software engineering capability, including implementing research-grade systems, experiment frameworks, structured logging, and reproducible workflows

Experience building secure data and model pipelines, including preprocessing, evaluation, and monitoring in sensitive or adversarial environments

Applied adversarial AI or threat-focused experience, such as red-teaming LLMs, vulnerability analysis, or evaluating model behavior under adversarial conditions

Ability to design and execute end-to-end experiments, from rapid prototyping through operational recommendations

Strong technical communication skills, including producing high-quality documentation and sponsor-facing deliverables