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Pacific Northwest National Laboratory

National lab advancing energy, security, environment

Lead Machine Learning Engineer 4

Natural Language Processing & LLMsPosted on Nov 9
Full-time$166.8k - $275.2k /yr

About the Job

Requirements

  • A BS/BA and at least 7 years of data science experience, an MS/MA and at least 5 years of data science experience, or a PhD and at least 3 years of data science experience is required.
  • Experience using Python with PyTorch, TensorFlow, or a similar deep learning framework is required.
  • Experience implementing or adapting methods for large language models, computer vision, or related deep learning applications is required.
  • U.S. citizenship is required.
  • Applicants must be able to obtain and maintain a federal security clearance and meet eligibility requirements for access to classified matter in accordance with 10 CFR 710, Appendix B.
  • Selected applicants are subject to pre-employment and random drug testing and must demonstrate non-use of illegal drugs, including marijuana, for the 12 consecutive months preceding completion of the required Questionnaire for National Security Positions.
  • Foreign national candidates who have not resided in the U.S. for three consecutive years are not eligible for the PIV credential and must obtain a favorable Local Site Specific Only federal risk determination to maintain employment.
  • Employees must obtain and maintain an HSPD-12 Personal Identity Verification credential and successfully complete the applicable federal background investigation and receive a favorable federal adjudication.
  • Applicants with an affiliation with a foreign government identified by the Department of Energy as a country of risk must disclose the affiliation and recuse themselves or receive approval from the Department of Energy and Battelle before their first day of employment.

Responsibilities

  • 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, resolving tradeoffs involving model and tool integration, workflow orchestration, reliability, and sponsor deployment constraints with researchers and engineers.
  • Work with researchers to interpret and adapt emerging methods, resolving tradeoffs among research intent, implementation behavior, performance, and deployment constraints.
  • Contribute directly to 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 to 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.

Desired Qualifications

  • 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.

About the Company

Pacific Northwest National Laboratory

National lab advancing energy, security, environment

Pacific Northwest National Laboratory (PNNL) conducts scientific research in chemistry, Earth sciences, biology, and data science to advance energy resiliency and national security. Its work spans designing and running experiments, developing models and simulations, and analyzing large data sets to generate knowledge and practical tools that help improve energy systems, environmental stewardship, and security. PNNL differentiates itself through its interdisciplinary approach across multiple scientific domains and its role as a government-funded national lab focused on real-world challenges, enabling collaborations with industry, academia, and government partners. Its goal is to produce actionable scientific insights and deployable technologies that strengthen national resilience and energy infrastructure.

Company size
1,001-5,000
Company stage
Not listed
Total funding
Not listed
Headquarters
Richland, WA
Founded
1965

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