Senior Data Scientist IV - AI Safety

** Overview*
* 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*
* The AI & Data Analytics division is seeking to hire a Senior Data Scientist for AI Safety applied research. This role blends foundational research with real‑world implementation to address some of the most complex and consequential challenges at the intersection of artificial intelligence and national security. We support PNNL's mission by contributing to impactful, mission‑focused applied R&D projects that help tackle national challenges.

** Work Location & Requirements*
* + Based in  
** Richland, WA
** or  
** Seattle, WA
** with  
** onsite presence required*
* +  
** Clearance
* * required (see below for details)

+ Travel may be required for stakeholder engagement or project events

** Key Responsibilities*
* As a Senior Data Scientist (Data Scientist
4), you will serve as a senior technical expert and leader setting technical direction and vision for AI Safety research, drive proposal development, mentor early through senior researchers, and engage directly with stakeholders.

Key responsibilities include:

+ Set technical direction for projects developing and applying safety methods and pipelines that analyze model internals (e.g., attention, routing, memory, tokenization, tool‑use) and/or surface failure modes, capabilities, and risk profiles.

+ Engage with stakeholders to translate mission needs and operational constraints into actionable safety requirements, test plans, and success criteria aligned with mission-driven goals.

+ Lead projects and programs to design, implement, and validate model evaluation for cutting edge AI including frontier AI systems (e.g. LLMs, LVMs, multimodal, agentic) or development/verification of safety controls and guardrails.

+ Translate cutting‑edge research into mission‑relevant tools and prototypes; rapidly evaluate new techniques and standards in AI safety, algorithm or system evaluation, and trustworthy AI.

+ Lead development of AI safety methods across multiple axes: robustness, uncertainty quantification, manipulation, autonomy and tool‑use risks, stress testing, generalization, or reliability under distribution shift.

+ Build effective relationships across teams and divisions; mentor junior and senior staff; cultivate an inclusive, collaborative research culture.

+ Maintain awareness of emerging trends in AI safety, AI security, alignment, national security operations, and relevant standards to shape future research directions.

** Knowledge, Skills, and Abilities*
* + Ability to collaborate in multi‑disciplinary, mission‑driven teams and operate effectively in high‑stakes

+

Demonstrated proficiency in leading proposals, writing technical reports, and communicating complex findings to diverse audiences (technical and executive).

+ Knowledge of compute environments and their cybersecurity concerns; familiarity with secure ML ops, access control, and model/data governance.

+ Deep knowledge of the…