Senior Data Scientist III- AI Assurance Researcher
Pacific Northwest National Laboratory, Seattle, WA, United States
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, Earth and Biological Sciences, Physical and Computational 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 the 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.
Responsibilities
PNNL is seeking a Senior Data Scientist – AI Assurance Researcher who has experience in understanding, exploring, and manipulating the internal mechanics and behaviors of AI models. 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.
Defines and leads research agendas in areas such as explainable AI, adversarial machine learning, AI safety, and the science of deep learning.
Designs and executes rigorous ML experiments at scale, including large‑scale training and evaluation on HPC infrastructure.
Develops novel evaluation methodologies that go beyond standard performance metrics to assess 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.
Interprets empirical results to identify promising research directions and guide strategic investment of team resources.
Establishes best practices for research code quality, reproducibility, and integration into operational pipelines.
Mentors junior researchers and engineers, fostering a culture of scientific rigor and collaborative inquiry.
Conducts work in secure environments with adherence to operational security requirements.
This position is based in either Richland, WA or Seattle, WA and requires an onsite presence.
Qualifications
Minimum Qualifications:
BS/BA and 5+ years of relevant work experience –OR–
MS/MA and 3+ years of relevant work experience –OR–
PhD with 1+ year of relevant experience
Preferred Qualifications:
Advanced degree in computer science, engineering, mathematics, or a related field.
Deep familiarity with the current ML research landscape, particularly in explainable AI, adversarial machine learning, AI safety, and the science of deep learning.
Track record of peer‑reviewed publications or technical contributions in relevant research areas.
Hands‑on experience analyzing the internal structures and representations of deep learning models, particularly large language models and large vision models.
Strong proficiency in PyTorch and associated deep learning libraries.
Experience designing and executing large‑scale experiments on HPC systems.
Demonstrated ability to translate research prototypes into deployable tools and capabilities.
Excellent communication skills, with the ability to convey complex research findings to both technical and non‑technical audiences.
Security Clearance Requirement
U.S. Citizenship.
Must obtain and maintain a federal security clearance, including a background investigation in accordance with 10 CFR 710, Appendix B.
Subject to pre‑employment and random drug testing for illegal drugs, including marijuana.
EEO Statement
We are an Equal Employment Opportunity employer. PNNL considers all applicants for employment without regard to race, religion, color, sex, national origin, age, disability, genetic information, protected veteran status, and any other status or characteristic protected by federal, state, and/or local laws. We are committed to providing reasonable accommodations for individuals with disabilities and disabled veterans.
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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, Earth and Biological Sciences, Physical and Computational 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 the 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.
Responsibilities
PNNL is seeking a Senior Data Scientist – AI Assurance Researcher who has experience in understanding, exploring, and manipulating the internal mechanics and behaviors of AI models. 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.
Defines and leads research agendas in areas such as explainable AI, adversarial machine learning, AI safety, and the science of deep learning.
Designs and executes rigorous ML experiments at scale, including large‑scale training and evaluation on HPC infrastructure.
Develops novel evaluation methodologies that go beyond standard performance metrics to assess 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.
Interprets empirical results to identify promising research directions and guide strategic investment of team resources.
Establishes best practices for research code quality, reproducibility, and integration into operational pipelines.
Mentors junior researchers and engineers, fostering a culture of scientific rigor and collaborative inquiry.
Conducts work in secure environments with adherence to operational security requirements.
This position is based in either Richland, WA or Seattle, WA and requires an onsite presence.
Qualifications
Minimum Qualifications:
BS/BA and 5+ years of relevant work experience –OR–
MS/MA and 3+ years of relevant work experience –OR–
PhD with 1+ year of relevant experience
Preferred Qualifications:
Advanced degree in computer science, engineering, mathematics, or a related field.
Deep familiarity with the current ML research landscape, particularly in explainable AI, adversarial machine learning, AI safety, and the science of deep learning.
Track record of peer‑reviewed publications or technical contributions in relevant research areas.
Hands‑on experience analyzing the internal structures and representations of deep learning models, particularly large language models and large vision models.
Strong proficiency in PyTorch and associated deep learning libraries.
Experience designing and executing large‑scale experiments on HPC systems.
Demonstrated ability to translate research prototypes into deployable tools and capabilities.
Excellent communication skills, with the ability to convey complex research findings to both technical and non‑technical audiences.
Security Clearance Requirement
U.S. Citizenship.
Must obtain and maintain a federal security clearance, including a background investigation in accordance with 10 CFR 710, Appendix B.
Subject to pre‑employment and random drug testing for illegal drugs, including marijuana.
EEO Statement
We are an Equal Employment Opportunity employer. PNNL considers all applicants for employment without regard to race, religion, color, sex, national origin, age, disability, genetic information, protected veteran status, and any other status or characteristic protected by federal, state, and/or local laws. We are committed to providing reasonable accommodations for individuals with disabilities and disabled veterans.
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