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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Lead Machine Learning Engineer IV - **Company:** Pacific Northwest National Laboratory - **Location:** Seattle, WA, United States - **Experience:** Expert - **Salary:** $166,800 - **Contract:** Temporary contract - **Skills:** Artificial Intelligence, Computer Vision, Automation of Tests, Code Review, Software Debugging, Python (Programming Language), Machine Learning, Software Architecture, Tensorflow, Software Engineering, Software Systems, Workflow Management Systems, Pytorch, Large Language Models, Deep Learning, Agentic-AI - **Published:** November 11, 2026 - **Apply:** https://dejobs.org/x/x/DC58546A33C042C9AF089FC8D35CFA27/job/ ## About the Role * BS/BA and 7+ years of data science experience -OR- * MS/MA and 5+ years of data science experience -OR- * PhD with 3+ years 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., * Experience leading software architecture, design, and implementation across multiple components and stakeholders, including guiding technical decisions and mentoring engineers. * Experience leading the development and evolution of agentic AI systems, including reusable components, evaluation, observability, and deployment across multiple contributors or projects. * Experience interpreting technical literature or mathematical descriptions, discussing assumptions with researchers, and implementing, adapting, or debugging methods for research and deployment, particularly in AI Assurance, agentic AI, or applied mathematics. * Experience maintaining and evolving shared software, including managing interfaces, dependencies, and integration changes, with practices for testing, reproducible development, code review, and releases. * Experience coordinating software development across organizations or with U.S. government stakeholders, deploying and improving software in sponsor environments, or developing technical plans and proposals for research or software development. * 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.eferral Eligible" ## Description PNNL is seeking a Lead Machine Learning Engineer to lead the development and evolution of software that brings emerging research methods into sponsor environments. You will work with researchers, engineers, and U.S. government stakeholders to shape technical approaches, resolve difficult implementation and integration challenges, and guide shared software through deployment and continued improvement. 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. You will lead through project teams, remain hands-on in technical work, and help engineers grow through mentoring, feedback, and opportunities to take on greater responsibility., * Set architecture and engineering direction for complex software capabilities involving multiple components, contributors, and stakeholder needs. * Guide the architecture and implementation of agentic AI systems across shared codebases, working with researchers and engineers to resolve tradeoffs involving model and tool integration, workflow orchestration, reliability, and sponsor deployment constraints. * Work with researchers to interpret and adapt emerging methods, resolving difficult tradeoffs among research intent, implementation behavior, performance, and deployment constraints. * Contribute directly to consequential implementation and debugging work, including deep learning methods in PyTorch. * Lead software evolution through development, deployment, and continued maintenance, coordinating technical plans, dependencies, and contributions across project teams. * Align interfaces and implementation approaches across shared codebases with researchers, engineers, and U.S. government stakeholders. * Guide testing and evaluation approaches that verify algorithmic behavior, integration, and reliability as methods and software change. * Guide reproducible development and release practices, including dependency management, automated testing, documentation, and compatibility across components. * Develop engineers through design discussions, code reviews, sustained mentoring, and opportunities to take on greater responsibility. * Shape technical plans and contribute to proposals with collaborators to support continued software development and new capabilities. * Conduct work in secure environments in accordance with operational security requirements.