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Pacific Northwest National Laboratory

National lab advancing energy, security, environment

Senior Machine Learning Engineer 3

Natural Language Processing & LLMsPosted on Nov 9
Full-time$140.2k - $228.8k /yrIn-person

About the Job

Requirements

  • A BS or BA and at least 5 years of data science experience, or an MS or MA and at least 3 years of data science experience, or a PhD and at least 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.
  • 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 demonstrated 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.
  • U.S. citizenship is required.
  • Applicants selected must meet eligibility requirements for access to classified matter in accordance with 10 CFR 710, Appendix B, and are subject to a federal background investigation.
  • Applicants selected are subject to pre-employment and post-employment random drug testing and must demonstrate non-use of illegal drugs, including marijuana, for the 12 consecutive months preceding completion of the Questionnaire for National Security Positions.

Responsibilities

  • Implement, adapt, and mature research methods into software deployed primarily in unclassified sponsor environments.
  • Work closely with researchers in SI Assurance, agentic SI, and applied mathematics to understand underlying methods and translate them into reliable implementations.
  • Develop software through experimental development, deployment, and continued improvement of software the team develops and maintains.
  • Guide engineering decisions and contribute directly to shared codebases in collaboration with researchers, engineers, and stakeholders across the U.S. government.
  • Design, develop, and implement methods, processes, and systems to analyze diverse data.
  • Apply 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 or unstructured data.
  • Produce solutions driven by exploratory data analysis from complex and high-dimensional datasets.
  • Design, develop, and evaluate predictive models and advanced algorithms to extract value from data.
  • Transfer skills across application domains.
  • Develop deep learning methods for large language models and computer vision, with opportunities to work on other models and methods.

Desired 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 machine-learning 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.

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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