Post Doctorate Research Associate - AI Assurance

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, Earth and Biological Sciences, Physical and Computational 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 the National Security Directorate, 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.
Responsibilities

We are seeking a Post Doctorate Researcher Associate with a focus on advancing the state-of-the-art in AI Assurance and AI applications in vision-based sensing developing rigorous methods and solutions to address mission-critical challenges.

Key responsibilities include:

• Collaborate with cross-disciplinary teams to integrate AI technologies into current and next-generation AI/ML solutions.
• Conduct cutting-edge research in support of national security missions.
• Mentor students and junior staff on project teams.

(AI Researcher Focus)

• Translate academic methods into deployable tools or research workflows to address critical challenges.
• Design and execute large-scale machine learning experiments (e.g., on HPC systems).
• Design, implement, and iterate on novel AI methods and applications.
• Use empirical results to guide iterative research and experimentation.
• Evaluate AI systems across multiple axes, including standard performance metrics and generalization to out-of-distribution data.

(AI Research Engineer Focus)

• Refactor, modularize, and optimize research code for maintainability and scalability.
• Build tools and APIs for seamless deployment and integration with unstructured code.
• Collaborate with researchers to understand algorithmic intent and with engineers to enable integration into broader systems.
• Develop tools and APIs to enable integrations into mission-relevant environments and workflows.

Qualifications

Minimum Qualifications:

• Candidates must have received a PhD within the past five years (60 months) or within the next 8 months from an accredited college or university.

Preferred Qualifications:

• Active DOE Q clearance with demonstrated ability to maintain such clearance.
• Active TS/SCI clearance with demonstrated ability to maintain such clearance.
• Proven experience applying advanced AI/ML/DL techniques to complex, real-world national security challenges, with measurable research or mission impact.
• Proficiency in Python software development, including deep expertise with core scientific libraries (e.g., Pandas, NumPy, SciPy) and ability to deliver efficient, production-quality code.
• Demonstrated track record of developing, training, and validating machine learning models using state-of-the-art frameworks (e.g., PyTorch, TensorFlow), with a focus on scalability and reproducibility.
• Hands-on experience applying machine learning and AI to specialized domains such as remote sensing, AI assurance, or high-stakes mission environments.
• Expertise in analyzing and interpreting deep learning model internals, particularly for large language models (LLMs) and advanced vision architectures, to improve transparency, performance, and trust.
• Strong communication skills with the ability to translate complex technical insights to diverse audiences, and a proven record of cross-functional collaboration with scientists, engineers, and mission stakeholders.
• Up-to-date knowledge of current ML/AI research frontiers (e.g., explainable AI, adversarial robustness, AI safety, and foundational deep learning science), with demonstrated ability to incorporate these advancements into applied projects.
• Demonstrated interest and ability in transitioning innovative research prototypes into mission-ready tools, platforms, or workflows that provide tangible end-user value.