Data Scientist II - AI Assurance Researcher

Data Scientist II - Ai Assurance Researcher 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. Key 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. This position is based in either Richland, WA or Seattle, WA and requires an onsite presence. Minimum Qualifications: BS/BA and 2 years of relevant experience -OR-

MS/MA -OR-

PhD Preferred Qualifications: 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. PNNL