Software Engineer II

Pacific Northwest National Laboratory•Richland, WA

9d•Onsite

About The Position

At PNNL, our core capabilities are divided among major departments that we refer to as Directorates within the Lab, focused on a specific area of scientific research or other function, with its own leadership team and dedicated budget. Our Science & Technology directorates include National Security, Integrated Discovery Sciences, and Energy and Environment. In addition, we have an Environmental Molecular Sciences Laboratory, a Department of Energy, Office of Science user facility housed on the PNNL campus. The National Security Directorate (NSD) drives science-based, mission-focused solutions to take on complex, real-world threats to our nation and the world. The AI and Data Analytics Division, part of NSD, combines profound domain expertise and creative integration of advanced hardware and software to deliver computational solutions that address complex data and analytic challenges. Working in multidisciplinary teams, we connect foundational research to engineering to operations, providing the tools to innovate quickly and field results faster. Our strengths are integrated across the data analytics lifecycle, from data acquisition and management to analysis and decision support.

Requirements

PhD -OR- MS/MA -OR- BS/BA and 2 years of software engineering experience
Hands-on professional experience using Python to build, test, and deliver software for customers or end users
Solid Python skills
Experience with one additional language (Go, Rust, C++, C#/.Net, or similar)
Foundational cloud experience (AWS, Azure, or GCP)
Familiarity with Linux environments
Understanding of version control (Git)
Exposure to CI/CD
Basic Agile/Scrum experience
Experience with SQL and understanding of data processing concepts
Experience using AI coding tools such as Claude Code, OpenAI Codex, GitHub Copilot, or similar to explore code, accelerate development, write tests, and debug effectively, with sound judgment around code quality, security, and validation.
U.S. Citizenship

Nice To Haves

Degree in computer science, software engineering, or related field
Experience in working on a team that developed and deployed petabyte size large-scale and high-performance ETL pipelines and analytics
Experience taking a project from idea or requirements through implementation, testing, and delivery to a customer, end user, or other stakeholder.
Demonstrated proficiency in Python and least one other language (Go, Rust, C++, C#/.Net, or similar)
Experience collaborating with a team of software engineers to design, build, and deliver complex software applications
Strong understanding of software engineering and data management best practices
Experience in cloud architecture (AWS, Azure, GCP) and implementation
Possession of professional certifications in cloud technologies or database certifications
Familiarity with machine learning algorithms with hands-on experience in machine learning pipeline development
Experience delivering software solutions for agencies such as the Department of Energy, Department of Homeland Security, Department of Defense, or National Nuclear Security Administration
Possession of an active Federal Q Clearance
Interest in AI/ML technologies, geospatial systems, large-scale data processing

Responsibilities

Collaborate with experienced engineers to design, build, and optimize scalable systems for processing multi-modal intelligence data at a remarkable scale and speed.
Contribute to mission-critical AI systems.
Help build enterprise-scale data platforms.
Gain hands-on experience with some of the nation's most sophisticated intelligence and security applications.
Receive mentorship from senior engineers.
Collaborate with research scientists.
See your code deployed in production systems that directly support national security missions.
Contribute to agentic AI implementations.
Work with LLM integrations.
Learn MLOps best practices on real-world applications.
Build components for multi-INT fusion systems.
Develop GEOINT processing tools.
Contribute to threat analysis platforms.
Develop data pipelines.
Build analytics workflows.
Contribute to cloud-native data architectures.
Receive mentorship from senior engineers.
Participate in code reviews.
Learn enterprise software development practices.