Lead Machine Learning Engineer IV

Pacific Northwest National Laboratory•Richland, WA

14h•Onsite

About The Position

Pacific Northwest National Laboratory (PNNL) is seeking a Lead Machine Learning Engineer to lead the development and evolution of software that brings emerging research methods into sponsor environments. The role works with researchers, engineers, and U.S. government stakeholders to shape technical approaches, resolve implementation and integration challenges, and guide shared software through deployment and ongoing improvement. The position contributes directly to shared codebases, from experimental implementation through deployment, primarily in unclassified sponsor environments. The team maintains and improves its software over time, focusing on deep learning methods for large language models and computer vision. Projects span AI Assurance, agentic AI, and applied mathematics, with opportunities to work with other models and methods. The role leads through project teams, remains hands-on in technical work, and supports engineers’ growth through mentoring, feedback, and increased responsibility. The position is based in Richland, Washington, or Seattle, Washington, and requires onsite presence. It requires the ability to obtain and maintain a federal security clearance and U.S. citizenship. Applicants selected for hire are subject to a federal background investigation and pre-employment and random drug testing. The position is a Testing Designated Position.

Requirements

Bachelor's degree and at least 7 years of data science experience, or master's degree and at least 5 years of data science experience, or Ph.D. and at least 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.
U.S. citizenship.
Ability to obtain and maintain a federal security clearance and meet eligibility requirements for access to classified matter.
Meet the stated drug-testing requirements, including demonstrating non-use of illegal drugs for the 12 consecutive months preceding completion of the required national security questionnaire.

Nice To Haves

Experience leading software architecture, design, and implementation across multiple components and stakeholders, including guiding technical decisions and mentoring engineers.
Experience leading 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.

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.
Work with researchers to interpret and adapt emerging methods, balancing research intent, implementation behavior, performance, and deployment constraints.
Contribute directly to implementation and debugging, 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.