Data Scientist - Evaluating Foundation Models and AI Agents for National Security

Overview At PNNL, our core capabilities are divided among major departments that we refer to as Directorates within the Lab, focused on a specific area of scientific research or other function, with its own leadership team and dedicated budget. Our Science & Technology directorates include National Security, Integrated Discovery Sciences, and Energy and Environment. In addition, we have an Environmental Molecular Sciences Laboratory, a Department of Energy, Office of Science user facility housed on the PNNL campus. The National Security Directorate (NSD) drives science-based, mission-focused solutions to take on complex, real-world threats to our nation and the world. The AI and Data Analytics Division, part of NSD, combines profound domain expertise and creative integration of advanced hardware and software to deliver computational solutions that address complex data and analytic challenges. Working in multidisciplinary teams, we connect foundational research to engineering to operations, providing the tools to innovate quickly and field results faster. Our strengths are integrated across the data analytics lifecycle, from data acquisition and management to analysis and decision support. Read more about the AI & Data Analytics division: https://www.pnnl.gov/ai-and-data-analytics Responsibilities As a Data Scientist in the AI and Data Analytics Division, you will work with experienced technical staff to design and implement evaluation studies, prepare mission-representative test data and scenarios, develop evaluation components and agentic harnesses, execute controlled experiments, and analyze model behavior. The work extends beyond aggregate benchmark scores to examine individual cases, meaningful data slices, component interactions, system performance under realistic operating conditions, and more. Depending on project needs, research may also address explainable AI, interpretability, or targeted mechanistic-interpretability questions. Key Responsibilities: • Implement defined test and evaluation activities for AI/ML with particular focus on pretrained transformer-based foundation models, including large language models (LLMs), vision-language models (VLMs), and the AI agents and systems that use them. • Prepare evaluation datasets, prompts, mission scenarios, meaningful data slices, test cases, scoring rubrics, and deterministic baselines. • Develop evaluation environments, test harnesses, and agentic harnesses used to exercise models, tools, retrieval systems, and end-to-end workflows. • Execute controlled experiments at the model, component, workflow, and system levels. • Analyze model behavior to identify capabilities, systematic weaknesses, failure modes, and sensitivity to data characteristics, retrieval results, tools, and operating conditions. • Examine agent trajectories, intermediate decisions, tool calls, retrieved evidence, memory or state, error propagation, recovery behavior, and points of human intervention. • Evaluate performance using aggregate metrics together with case-level analysis, behavioral categories, robustness testing, calibration and uncertainty characterization, and adversarial or off-nominal scenarios. • Evaluate hybrid systems that combine generative AI with deterministic NLP, computer vision, retrieval, search, rules, or classical machine-learning methods. • Help develop and validate automated or model-based graders against human judgments and mission-relevant criteria. • Apply functional explainability, model probing, or selected interpretability methods for understanding model and system behavior. • Document evaluation methods, assumptions, results, uncertainties, and limitations in reproducible code, visualizations, technical reports, and briefings. • Contribute technical material, data, figures, demonstrations, or preliminary results to proposals and sponsor engagements. Work Location & Requirements: • This position is based in Richland, WA or Seattle, WA and requires an onsite presence. • This position requires the ability to obtain and maintain a federal security clearance. (see below for details) Qualifications Minimum Qualifications: • BS/BA and 2 years of data science experience -OR- • MS/MA -OR- • PhD • Experience developing or evaluating LLMs, VLMs, pretrained transformer models, AI agents, or integrated AI systems. • Experience using Python and machine-learning frameworks. Preferred Qualifications: • Experience building evaluation datasets, test harnesses, agentic harnesses, automated graders, behavioral tests, or analysis pipelines. • Experience analyzing model behavior, failure modes, robustness, calibration, uncertainty, or performance across meaningful data slices. • Familiarity with agent orchestration frameworks such as LangGraph or similar technologies involving routing, retrieval, tools, memory, state, and human oversight. • Experience with deterministic NLP, computer vision, retrieval, search, rules, or classical machine-learning methods that complement generative AI. • Experience in one or more related areas such as explainable AI, model probing, mechanistic interpretability, AI security, red teaming, uncertainty quantification, or privacy-preserving AI. • Experience contributing to applied research through technical reports, publications, prototypes, or open-source software. • Prior experience supporting government, national security, or other high-consequence applications. • Active TS/SCI clearance. Hazardous Working Conditions/Environment • Travel may be required for project events. Additional Information This position requires the ability to obtain and maintain a federal security clearance. A security clearance background investigation includes review of your employment, education, financial, and criminal history, as well as interviews with you and your personal references, neighbors, and co-workers to determine trustworthiness, reliability, and loyalty to the United States. The investigation also examines y