Machine Learning Engineer II

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, Integrated Discovery 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 Machine Learning Engineer to implement and adapt research methods within shared software used by U.S. government stakeholders. You will work with researchers in areas such as AI Assurance, agentic AI, and applied mathematics, independently developing and maintaining defined software components within a broader engineering effort. You will contribute directly to shared codebases, from experimental implementation through deployment primarily in unclassified sponsor environments. The team maintains and improves the software over time. The work focuses on deep learning methods for large language models and computer vision. Projects span research areas such as AI Assurance, agentic AI, and applied mathematics, with opportunities to work with other models and methods. Key Responsibilities • Implement and adapt deep learning and computational methods with researchers, investigating algorithmic assumptions and debugging implementation and model behavior. • Independently develop, test, and maintain defined software components or features, making design decisions within the broader architecture. • Implement, test, and maintain components of agentic AI workflows, including model tool calling and integration with APIs or data sources, making implementation decisions within the broader software architecture. • Build reusable packages, APIs, and interfaces that connect research methods with sponsor systems and workflows. • Refactor experimental code and improve its clarity, performance, and maintainability. • Develop tests and evaluations that compare implementations with research results and verify behavior as software changes. • Use reproducible environments, dependency management, automated testing, and documentation to support collaborative development. • Work with other engineers to integrate and deploy components, resolve defects, and incorporate research advances and stakeholder feedback. • Coordinate changes in shared codebases, participate in code reviews, and share methods and findings with colleagues. • Share implementation approaches and findings with researchers and engineers working in the broader effort. • Conduct work in secure environments in accordance with 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 2 years of data science experience -OR- • MS/MA -OR- • PhD • 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 with modular software design, automated testing, version control, and collaborative development. • Experience building or adapting agentic AI applications, including workflow orchestration, tool integration, or tracing and evaluating multi-step behavior. • Experience interpreting technical literature or mathematical descriptions and collaborating with researchers to implement methods and evaluate results, particularly in AI Assurance, agentic AI, or applied mathematics. • Experience developing reusable packages or APIs and integrating them into larger systems. • Experience deploying and maintaining ML or computational software, 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 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 com