Requisition Id 16618

Overview:

We are seeking a highly motivated Research Engineer who will support agentic AI workflows, AI infrastructure architectures, and AI-native applications for critical infrastructure resilience projects at ORNL. This position resides in the Critical Infrastructure Resilience (CIR) Group in the Human Dynamics Section, Geospatial Science and Human Security Division, National Security Sciences Directorate, at Oak Ridge National Laboratory (ORNL).

As a Research Engineer, you will contribute to cutting-edge research and development efforts aimed at enhancing the resilience of critical infrastructure systems through AI-enabled decision support, intelligent data agents, retrieval-augmented generation, and scalable machine learning systems. This position offers a unique opportunity to work on interdisciplinary projects in collaboration with leading experts, tackling challenges related to energy, water, cybersecurity, transportation, emergency response, and community resilience. You will have the opportunity to creatively use methods from generative AI, agentic AI systems, computational data science, machine learning, high-performance computing, cloud platforms, and geospatial analytics to help frame and solve these problems on a national and global scale.

The successful candidate will work on advancing AI architectures, data pipelines, model evaluation methods, and production-ready applications that improve understanding of critical infrastructure risks, dependencies, and recovery options. Your research will help inform investments in resilient infrastructure; support AI-enabled planning, restoration, and emergency response workflows; accelerate analysis of heterogeneous infrastructure datasets; and deliver trustworthy, secure, and reproducible AI capabilities for mission-driven sponsors.

The CIR Group is a part of the Geospatial Science and Human Security Division at ORNL. The CIR group is heavily engaged in modeling risk and resilience of critical infrastructures to achieve equitable, reliable and adaptable built environments through data ecosystems creation, data science and integrated complex systems analysis. The group's vision is to enable a sustainable, safe, and secure critical infrastructure for all.

Major Duties/Responsibilities:

• Lead and contribute to the design, development, and implementation of agentic AI workflows, retrieval-augmented generation systems, LLM orchestration patterns, and AI-native applications that improve understanding of critical infrastructure resilience, risk, and recovery.

• Design and evaluate scalable AI infrastructure architectures for critical infrastructure applications, including data ingestion, vector search, model serving, workflow orchestration, monitoring, and deployment across high-performance computing, cloud, and hybrid environments.

• Develop intelligent data agents and automated analysis tools that can explore, summarize, validate, and visualize large, heterogeneous datasets while supporting transparent and uncertainty-aware decision-making.

• Investigate how AI-enabled tools can improve resilience planning, operational awareness, and emergency response for energy, water, cyber, transportation, and community systems, with attention to reliability, security, equity, human oversight, and responsible AI practices.

• Analyze large-scale datasets from diverse sources to extract insights relevant to infrastructure vulnerability, interdependency, disruptions, restoration, and community impacts. Apply machine learning, deep learning, natural language processing, graph learning, benchmarking, and evaluation techniques to build robust, reproducible models and applications.

• Contribute to and lead team publications, technical reports, software prototypes, demonstrations, and sponsor briefings; participate in conferences; and engage with scientists, analysts, software engineers, and mission partners in the private sector, academia, national laboratories, and US Government communities.

Basic Qualifications:

• Ph.D. in computer science, computational data science and engineering, computer engineering, artificial intelligence, machine learning, applied mathematics, statistics, geospatial science, engineering, or an equivalent field with 0-2 years of research experience.

• Strong background in artificial intelligence, machine learning, data science, software engineering, quantitative analysis, and/or scientific computing, with demonstrated ability to translate research methods into working applications or computational workflows.

• Demonstrated experience with agentic AI, generative AI, retrieval-augmented generation, LLM orchestration, prompt engineering, intelligent data agents, or automated data exploration and summarization workflows.

• Proficiency in Python and experience with relevant AI/ML frameworks and tools such as PyTorch, TensorFlow, scikit-learn, Hugging Face, LangChain, LlamaIndex, vector databases, MLflow, or comparable libraries and platforms.

• Experience acquiring, integrating, storing, and analyzing large structured and unstructured datasets using databases, data engineering tools, or cloud/HPC storage systems; familiarity with SQL, Spark, Dask, Pandas, NumPy, Snowflake, AWS S3, MongoDB, or comparable technologies.

• Excellent written and oral communication skills, including peer-reviewed publications, technical documentation, or software demonstrations, with the ability to collaborate effectively with colleagues from diverse backgrounds and present technical information to technical and non-technical audiences.

• Proven ability to work independently and as part of a team, with a strong commitment to reproducible research, secure and reliable software practices, project goals, mentoring when appropriate, and delivery of high-quality results.

Preferred Qualifications:

• Knowledge of critical infrastructure systems, including energy, water, transportation, cyber, telecommunications, and emergency management, and how AI-enabled tools can support their analysis, operation, planning, and resilience.

• Publication record, technical portfolio, open-source contributions, or conference activity demonstrating contributions in AI/ML, generative AI, scientific computing, data-intensive applications, MLOps, or critical infrastructure analytics.

• Experience developing, deploying, or maintaining production-oriented AI/ML systems using MLOps, CI/CD, Docker, Kubernetes, model serving, workflow orchestration, monitoring, benchmarking, and evaluation practices.

• Experience with cloud, high-performance computing, or GPU-accelerated workflows, including AWS, Azure, GCP, GCP Vertex AI, Amazon SageMaker, CUDA, DeepSpeed, or comparable scalable training and inference environments.

• Experience working collaboratively in version control systems for source code management such as Git/GitLab and using reproducible workflows for shared research software, AI pipelines, documentation, and deployment artifacts.

• Knowledge of major challenges in applying AI to real-world mission systems, including data quality, uncertainty, model evaluation, explainability, human-in-the-loop workflows, cybersecurity, privacy, scalability, and responsible AI governance.

• Ability to function well in a fast-paced research environment, set priorities to accomplish multiple tasks within deadlines, and adapt to evolving sponsor, mission, and technology needs.

Security, Credentialing, and Eligibility Requirements:

For employment at Oak Ridge National Laboratory (ORNL), a Real ID compliant form of identification will be required. Additionally, ORNL is subject to Department of Energy (DOE) access restrictions. All employees must also be able to obtain and maintain a federal Personal Identity Verification (PIV) card as mandated by Homeland Security Presidential Directive 12 (HSPD-12) and Department of Energy (DOE) Order 473.1A, which requires a favorable post-employment background investigation.

To obtain this credential, new employees must successfully complete and pass a Federal Tier 1 background check investigation. This investigation includes a declaration of illegal drug activities, including use, supply, possession, or manufacture within the last year. This includes marijuana and cannabis derivatives, which are still considered illegal under federal law, regardless of state laws.

For foreign national candidates:

If you have not resided in the U.S. for three consecutive years, you are not eligible for the PIV credential and instead will need to obtain a favorable Local Site Specific Only (LSSO) risk determination to maintain employment. Once you meet the three-year residency requirement, you will be required to obtain a PIV credential to maintain employment.