Senior Data Scientist IV - AI Safety

Pacific Northwest National Laboratory•Richland, WA

3d•Onsite

About The Position

The AI & 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. 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.

Requirements

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 current ML research landscape, especially AI safety, adversarial machine learning, xAI/interpretability, uncertainty quantification, and the science of deep learning.
Hands‑on experience analyzing internal structures of deep learning models, particularly LLMs and large vision/multimodal models (tokenization, attention mechanisms, routing, weight adaptation, loss optimization) or modalities including hyperspectral imagery.
Experience designing and executing T&E campaigns for AI systems, including test planning, dataset curation, metrics, statistical analysis, and reproducibility.
Software engineering foundations: Python, ML frameworks (PyTorch/TensorFlow), experiment tracking, and data pipeline tooling.
Strong stakeholder engagement skills and the ability to connect technical safety work to operational mission outcomes.
Proven track record delivering AI safety/evaluation work products in complex domains, including National Security.
Hands‑on experience with LLM/LVM/Foundation Model and Frontier AI evaluation, red‑teaming, uncertainty analysis, or safety control implementation.
Experience leading federally funded R&D projects, with publications, open‑source contributions, or deployable prototypes.
BS/BA and 7+ years of relevant work experience -OR- MS/MA and 5+ years of relevant work experience -OR- PhD with 3+ years of relevant experience
U.S. Citizenship
Background Investigation: Applicants selected will be subject to a Federal background investigation and must meet eligibility requirements for access to classified matter in accordance with 10 CFR 710, Appendix B.
Drug Testing: All Security Clearance positions are Testing Designated Positions, which means that the applicant selected for hire is subject to pre-employment drug testing, and post-employment random drug testing. In addition, applicants 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).
Note: Applicants will be considered ineligible for security clearance processing by the U.S. Department of Energy if non-use of illegal drugs, including marijuana, for 12 months cannot be demonstrated.

Nice To Haves

Experience serving as PI, technical lead, or project manager on multi‑institution R&D efforts.
Demonstrated impact in safe and trustworthy AI (e.g., peer‑reviewed publications, recognized awards, contributions to community standards).
Familiarity with agentic AI safety (tool‑use governance, retrieval hygiene, autonomous decision workflows) and evaluation of multi‑step reasoning systems.
Experience with privacy‑preserving ML (e.g., differential privacy, federated learning), robust training methods, and secure data lifecycle practices.
Background working with or mapping to relevant frameworks/standards (e.g., AI safety/testing best practices, risk management frameworks) and translating them into test and evaluation plans.
Prior work in national security environments, with an understanding of mission needs and operational constraints.
Active DOE Q or TS/SCI clearance.

Responsibilities

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.