Explainable AI - Postdoctoral Researcher

In-depth knowledge in explainable AI and related topics, demonstrate relevant experiences and corresponding publications.

Demonstrated research experience in explainable or interpretable AI, representation learning, mechanistic interpretability, concept-based explanation, or visual analytics for machine learning.

Experience developing and applying deep learning methods at medium to large scale using modern libraries such as PyTorch or JAX.

Demonstrated research productivity, as documented by publications, reports, presentations, and/or open-source software in relevant venues (NeurIPS, ICML, ICLR, CVPR, ACL, IEEE VIS, CHI, JMLR, etc.).

Experience with scientific programming in the Python ecosystem, and demonstrated ability to obtain substantial domain knowledge in fields of application in order to communicate effectively with subject matter experts.

Experience analyzing or intervening on the internal representations of trained models, such as probing for encoded properties, steering or editing activations to alter behavior, or attributing outputs to internal components.

All your information will be kept confidential according to EEO guidelines.

Position Information

This is a Postdoctoral appointment with the possibility of extension to a maximum of three years, open to those who have been awarded a PhD at time of hire date.

Why Lawrence Livermore National Laboratory?

Included in 2026 Best Places to Work by Glassdoor!

Flexible

in Explainable AI to contribute to fundamental R&D on understanding what modern AI models learn and how that knowledge is represented internally.

As foundation models and deep surrogates inform consequential scientific and national security decisions, domain experts need to inspect, validate, and steer model internals, making interpretability as much a human-AI collaboration problem as a modeling one.

Applications area includes but not limited to multimodal SciML models and deep surrogates for various simulations.

Design human-in-the-loop workflows that let domain experts explore, validate, and correct discovered concepts, and evaluate those workflows with real users.

Establish rigorous evaluation methodology for interpretability claims, i.e., faithfulness, stability, and causal grounding.

Research, design, implement, and apply advanced machine learning methods for multiple applications in a collaborative scientific environment.

Join us and make YOUR mark on the World!

Lawrence Livermore National Laboratory (LLNL) has turned bold ideas into world-changing impact advancing science and technology to strengthen U.S. security and promote global stability.

Our mission spans four critical national security areas nuclear deterrence, threat preparedness, energy security, and multi-domain defense empowering teams to take on the toughest challenges of today and tomorrow. With a culture built on innovation and operational excellence, LLNL is a place where your expertise can make a real impact.