Machine Learning Engineer II

Pacific Northwest National Laboratory•Richland, WA

1d•Onsite

About The Position

Pacific Northwest National Laboratory (PNNL) is seeking a Machine Learning Engineer to implement and adapt research methods in shared software used by U.S. government stakeholders. The role works with researchers in areas including AI Assurance, agentic AI, and applied mathematics, independently developing and maintaining defined software components as part of a broader engineering effort. The work focuses on deep learning methods for large language models and computer vision, with opportunities to work with additional models and methods. The engineer contributes to shared codebases from experimental implementation through deployment, primarily in unclassified sponsor environments. The team maintains and improves the software over time. The position is based in Richland, Washington, or Seattle, Washington, and requires an onsite presence. The role requires the ability to obtain and maintain a federal security clearance and U.S. citizenship. Selected candidates are subject to federal background investigation and pre-employment and random drug testing.

Requirements

BS/BA and 2 years of data science experience, or an MS/MA, or a PhD.
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.
Ability to obtain and maintain a federal security clearance.
U.S. citizenship.
Selected applicants must meet eligibility requirements for access to classified matter under 10 CFR 710, Appendix B.
Selected applicants must be able to demonstrate non-use of illegal drugs, including marijuana, for the 12 consecutive months preceding completion of the QNSP.

Nice To Haves

Experience with modular software design, automated testing, version control, and collaborative development.
Experience building or adapting agentic AI applications, including workflow orchestration, tool integration, or tracing and evaluating multi-step behavior.
Experience interpreting technical literature or mathematical descriptions and collaborating with researchers to implement methods and evaluate results, particularly in AI Assurance, agentic AI, or applied mathematics.
Experience developing reusable packages or APIs and integrating them into larger systems.
Experience deploying and maintaining ML or computational software, including reproducible environments, dependency management, automated build and deployment workflows, and incorporating user feedback.
Experience supporting national security missions.

Responsibilities

Implement and adapt deep learning and computational methods with researchers; investigate algorithmic assumptions and debug implementations and model behavior.
Independently develop, test, and maintain defined software components or features, making design decisions within the broader architecture.
Implement, test, and maintain components of agentic AI workflows, including model tool calling and integration with APIs or data sources.
Build reusable packages, APIs, and interfaces connecting research methods with sponsor systems and workflows.
Refactor experimental code to improve clarity, performance, and maintainability.
Develop tests and evaluations that compare implementations with research results and verify behavior as software changes.
Use reproducible environments, dependency management, automated testing, and documentation to support collaborative development.
Work with other engineers to integrate and deploy components, resolve defects, and incorporate research advances and stakeholder feedback.
Coordinate changes in shared codebases, participate in code reviews, and share methods and findings with colleagues.
Share implementation approaches and findings with researchers and engineers in the broader effort.
Conduct work in secure environments in accordance with operational security requirements.