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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Machine Learning Engineer II - **Company:** Pacific Northwest National Laboratory - **Location:** Seattle, WA, United States - **Experience:** Experienced - **Salary:** $114,000 - **Contract:** Temporary contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Computer Vision, Automation of Tests, Code Review, Computer Simulation, Software Debugging, Python (Programming Language), Machine Learning, Software Architecture, Tensorflow, Software Engineering, Workflow Management Systems, Pytorch, Large Language Models, Deep Learning, Agentic-AI, Code Restructuring, Software Version Control - **Published:** November 11, 2026 - **Apply:** https://dejobs.org/x/x/507C2DD703D94661912B7D277BD57E1A/job/ ## About the Role * 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., * 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 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. ## Description 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., * 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.