Senior Machine Learning Engineer

Pacific Northwest National Laboratory•Richland, WA

155d•Onsite

About The Position

PNNL is seeking a Senior Machine Learning Engineer who has deep experience refactoring and modularizing research code for maintainability, extensibility, and reusability. The selected candidate must be able to collaborate effectively across research and engineering teams to align research goals with deployment requirements, and to develop packages, APIs, and interfaces that enable straightforward integration into mission-relevant environments. They should be fluent in Python and modern ML frameworks, and comfortable working with unstructured, experimental code. This position is based in either Richland, WA or Seattle, WA and requires an onsite presence.

Requirements

BS/BA and 5+ years of relevant work experience -OR- MS/MA and 3+ years of relevant work experience -OR- PhD with 1+ year of relevant experience
U.S. Citizenship
Ability to obtain and maintain a federal security clearance.
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).

Nice To Haves

Degree in computer science, engineering, mathematics, or a related field.
Experience in research engineering, ML engineering, AI systems integration, or applied data science.
Strong proficiency in Python and hands-on experience with ML frameworks (e.g., PyTorch, TensorFlow, Hugging Face).
Demonstrated ability to navigate research codebases (e.g., Jupyter notebooks, unstructured scripts) and translate them into production-ready components.
Experience designing and deploying scalable ML pipelines or AI-enabled tools in operational or mission-critical settings.
Excellent communication and cross-functional collaboration skills, with the ability to bridge research and engineering teams.

Responsibilities

Leads the refactoring, modularization, and optimization of research code to improve maintainability, scalability, and production readiness.
Collaborates closely with researchers to understand algorithmic intent and with engineers to ensure seamless integration into broader systems and workflows.
Architects and develops tools, pipelines, and APIs that enable deployment into mission-relevant environments.
Influences technical roadmaps and architectural decisions for AI/ML infrastructure.
Evaluates and recommend emerging tools, frameworks, and practices to keep the team at the leading edge.
Establishes and promotes best practices for translating research outputs into robust, production-quality software.
Mentors junior staff on software engineering standards, code quality, and research-to-production workflows.
Writes clear, well-documented code and leads code reviews to uphold team standards.
Conducts work in secure environments with adherence to operational security requirements.