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Pacific Northwest National Laboratory

National lab advancing energy, security, environment

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

Natural Language Processing & LLMsUpdated on Nov 13
Full-time$114k - $182.1k /yr

About the Job

Requirements

  • A Bachelor of Science or Bachelor of Arts degree and two years of data science experience, or a Master of Science or Master of Arts degree, or a PhD.
  • Experience developing or evaluating large language models, vision-language models, pretrained transformer models, AI agents, or integrated AI systems.
  • Experience using Python and machine-learning frameworks.
  • The ability to obtain and maintain a federal security clearance.
  • U.S. citizenship is required.
  • Applicants must meet eligibility requirements for access to classified matter under 10 CFR 710, Appendix B.
  • Selected applicants are subject to pre-employment and post-employment random drug testing and must demonstrate 12 consecutive months of non-use of illegal drugs, including marijuana, before completing the Questionnaire for National Security Positions.
  • Applicants must meet the federal background-investigation and credentialing requirements, including applicable requirements for a federal Personal Identity Verification credential.
  • Employees and contractors must disclose any affiliation with a foreign government designated by the Department of Energy as a country of risk and either end the affiliation or obtain approval before starting work.

Responsibilities

  • 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.
  • Implement defined test and evaluation activities for artificial intelligence and machine learning, particularly pretrained transformer-based foundation models, including large language models, vision-language models, and the 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 for exercising models, tools, retrieval systems, and end-to-end workflows.
  • Execute controlled experiments at 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 and case-level analysis, behavioral categories, robustness testing, calibration and uncertainty characterization, and adversarial or off-nominal scenarios.
  • Evaluate hybrid systems combining generative artificial intelligence with deterministic natural language processing, 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 to understand 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.
  • Travel to project events when required.

Desired 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 natural language processing, computer vision, retrieval, search, rules, or classical machine-learning methods that complement generative artificial intelligence.
  • Experience in one or more related areas such as explainable artificial intelligence, model probing, mechanistic interpretability, AI security, red teaming, uncertainty quantification, or privacy-preserving artificial intelligence.
  • 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.

About the Company

Pacific Northwest National Laboratory

National lab advancing energy, security, environment

Pacific Northwest National Laboratory (PNNL) conducts scientific research in chemistry, Earth sciences, biology, and data science to advance energy resiliency and national security. Its work spans designing and running experiments, developing models and simulations, and analyzing large data sets to generate knowledge and practical tools that help improve energy systems, environmental stewardship, and security. PNNL differentiates itself through its interdisciplinary approach across multiple scientific domains and its role as a government-funded national lab focused on real-world challenges, enabling collaborations with industry, academia, and government partners. Its goal is to produce actionable scientific insights and deployable technologies that strengthen national resilience and energy infrastructure.

Company size
1,001-5,000
Company stage
Not listed
Total funding
Not listed
Headquarters
Richland, WA
Founded
1965

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