Senior Machine Learning Engineer III

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 National Security Directorate, 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 Machine Learning Engineer to implement, adapt, and mature research methods into software deployed primarily in unclassified sponsor environments. You will work closely with researchers in areas such as SI Assurance, agentic SI, and applied mathematics, understanding the underlying methods and translating them into reliable implementations. Your work will span experimental development, deployment, and continued improvement of software the team develops and maintains. You will guide engineering decisions and contribute directly to shared codebases, collaborating with researchers, engineers, and stakeholders across the U.S. government. The work focuses on deep learning methods for large language models and computer vision. Projects span research areas such as SI Assurance, agentic SI, and applied mathematics, with opportunities to work with other models and methods. Key Responsibilities • Designs, develops, and implements methods, processes, and systems to analyze diverse data. • Applies knowledge of statistics, machine learning, advanced mathematics, simulation, software development, and data modeling to integrate and clean data, recognize patterns, address uncertainty, pose questions, and make discoveries from structured and/or unstructured data. • Produces solutions driven by exploratory data analysis from complex and high-dimensional datasets. • Designs, develops, and evaluates predictive models and advanced algorithms that lead to optimal value extraction from the data. • Demonstrates ability to transfer skills across application domains. • Strong proficiency in Python and hands-on experience using PyTorch to implement and modify deep learning research methods, including investigating and debugging implementation and model behavior. • Professional experience developing and maintaining software in a collaborative codebase. Significant contributions to established open-source software with a demonstrated record of use and review may also qualify. • Experience with software design, modular implementation, automated testing, version control, and code review. • Experience owning the design, development, and maintenance of substantial software components or capabilities, and making implementation decisions within a project. • Experience collaborating with researchers and engineers to translate technical requirements into working software and make implementation decisions. Qualifications Minimum Qualifications: • BS/BA and 5+ years of data science experience -OR- • MS/MA and 3+ years of data science experience -OR- • PhD with 1+ year of data science experience • Experience using Python with PyTorch, TensorFlow, or a similar deep learning framework. • Experience implementing or adapting methods for large language models, computer vision, or related deep learning applications. Preferred Qualifications: • Experience leading software design or implementation efforts and mentoring other engineers. • Experience developing and evaluating agentic AI systems, including reusable tool interfaces, workflow state management, execution tracing, or systematic analysis of task outcomes and failures. • Experience interpreting technical literature or mathematical descriptions and collaborating with researchers to translate methods into working software, particularly in AI Assurance, agentic AI, or applied mathematics. • Experience developing modular, reusable packages or APIs, integrating them into larger systems, and using automated testing, version control, and collaborative development practices. • Experience deploying, maintaining, and improving ML or computational software in shared codebases, including reproducible environments, dependency management, automated build and deployment workflows, and incorporating user feedback. • Experience supporting national security missions. Hazardous Working Conditions/Environment Not Applicable 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 your foreign connections, drug and alcohol use, foreign influence, and overall conduct. Requirements: • 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