Data Scientist II - AI Assurance Researcher

Pacific Northwest National Laboratory•Richland, WA

5h•$114,000 - $182,100•Onsite

About The Position

PNNL is seeking a Data Scientist II - AI Assurance Researcher who has experience in understanding, exploring, and manipulating the internal mechanics and behaviors of AI models and can provide valuable insights into the decision boundaries and mathematical fingerprints of data properties. The selected candidate should have extensive experience training models, accessing and working with data embeddings, the ability to derive theoretical queries from empirical results, and demonstrate the capacity to translate research papers and findings into mission relevant insights and tools. This position is based in either Richland, WA or Seattle, WA and requires an onsite presence.

Requirements

BS/BA and 2 years of relevant experience -OR- MS/MA -OR- PhD
U.S. Citizenship
Background Investigation: Applicants selected will be subject to a Federal background investigation and must meet eligibility requirements for access to classified matter in accordance with 10 CFR 710, Appendix B.
Drug Testing: All Security Clearance positions are Testing Designated Positions, which means that the applicant selected for hire is subject to pre-employment drug testing, and post-employment random drug testing. In addition, applicants must be able to demonstrate non-use of illegal drugs, including marijuana, for the 12 consecutive months preceding completion of the requisite Questionnaire for National Security Positions (QNSP).
Note: Applicants will be considered ineligible for security clearance processing by the U.S. Department of Energy if non-use of illegal drugs, including marijuana, for 12 months cannot be demonstrated.
Testing Designated Position
This position is a Testing Designated Position (TDP). The candidate selected for this position will be subject to pre-employment and random drug testing for illegal drugs, including marijuana, consistent with the Controlled Substances Act and the PNNL Workplace Substance Abuse Program.
Mandatory Requirements
Please be aware that the Department of Energy (DOE) prohibits DOE employees and contractors from having any affiliation with the foreign government of a country DOE has identified as a “country of risk” without explicit approval by DOE and Battelle. If you are offered a position at PNNL and currently have any affiliation with the government of one of these countries, you will be required to disclose this information and recuse yourself of that affiliation or receive approval from DOE and Battelle prior to your first day of employment.

Nice To Haves

Degree in computer science, engineering, mathematics, or a related field.
Familiarity with the current ML research landscape, particularly in explainable AI, adversarial machine learning, AI safety, and the science of deep learning.
Hands-on experience analyzing the internal structures of deep learning models, particularly large language models and large vision models.
Proficiency in PyTorch and associated deep learning libraries.
Experience designing and executing experiments on HPC systems.
Experience in translating research prototypes into deployable tools and capabilities.
Demonstrated strong communication and collaboration skills, with the ability to work effectively in a multi-disciplinary team environment.

Responsibilities

Designs and executes rigorous ML experiments following community best practices, including large-scale training and evaluation on HPC infrastructure.
Evaluates AI systems across multiple dimensions, including standard performance metrics, generalization, robustness, and out-of-distribution behavior.
Analyzes the internal structures and representations of deep learning models, with emphasis on large language models and large vision models.
Assesses empirical results to identify trends, inform research direction, and recommend next steps.
Writes high-quality, well-documented research code that supports reproducibility and collaboration.
Contributes to publications and technical reports that communicate findings to both internal and external audiences.
Collaborates with senior researchers, engineers, and cross-functional teams to integrate research into broader systems and workflows.
Conducts work in secure environments with adherence to operational security requirements.