Artificial Intelligence Research Engineer

The Artificial Intelligence Research Engineer is part of the Wicked Problems Laboratory within the Institute of National Security at Vanderbilt University and is a key technical contributor focused on advancing research and development in agentic AI frameworks, large-scale data systems, and AI-driven analytic tools for national security applications. This position is deeply technical, emphasizing the construction, testing, and optimization of systems that integrate diverse application programming interfaces (APIs), large language models (LLMs), and data pipelines to support adaptive, mission-critical workflows.

The role blends AI engineering with applied research execution— programming, and coordinating agentic AI architectures, developing analytic tools, and evaluating emerging threats. Strong capability in AI model development, secure system design, and generative AI analysis is essential, particularly in contexts shaped by adversarial pressures and evolving national security challenges.

Reporting to the Head of the Wicked Problems Lab, the AI Research Engineer collaborates with research scientists, engineers, and faculty to assess threat landscapes, strengthen cyber resiliency, and accelerate the adoption of secure, advanced technologies across government, industry, and public-sector infrastructures.

Key Functions and Expected Performance:
• 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 and interdisciplinary teams to support grant objectives, publications, prototypes, and demonstrations.
• Present technical insights through briefings, reports, and presentations to academic, industry, and operational audiences.

Supervisory Relationships:
This position does not have supervisory responsibility; this position reports administratively and functionally to the Executive Director of the Institute of National Security.

Education and Certifications:
• Bachelor’s degree in Computer Science, Computer Engineering, Data Science, or a related technical field is required.
• Master’s degree in related field is preferred.

Required Experience and Skills:
• 1–3+ 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.
• 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.

Preferred Experience & Skills
• Knowledge of large-scale data systems, distributed computing, GPU optimization, or containerized AI environments (Docker, Kubernetes).
• Experience building or evaluating agentic AI systems—multi-agent architectures, planning/decision-making agents, or autonomous workflows.

Security Clearance
• Eligibility for a U.S. security clearance is strongly preferred.