Explainable AI - Postdoctoral Researcher

Lawrence Livermore National Laboratory; Livermore (CA); Hybrid; USD 122,000 - 165,000; Full time

Job summary

Lawrence Livermore National Laboratory invites applications for a Postdoctoral Researcher in Explainable AI in Livermore, CA. The role focuses on understanding how modern AI models learn and how to interpret their internal representations, including sparse decompositions and concept discovery.

The position supports human-in-the-loop workflows, rigorous evaluation of interpretability, and independent, collaborative ML research with national security applications. Hybrid work options expected.

Qualifications

• Recent Ph.D. in Computer Science, Machine Learning, Applied Mathematics, Statistics, Human-Computer Interaction, or a related field.
• In-depth knowledge in explainable AI and related topics, demonstrate relevant experiences and publications.
• Demonstrated research experience in explainable or interpretable AI, representation learning, mechanistic interpretability, concept-based explainability, or visual analytics for ML.
• Experience developing and applying deep learning methods at medium to large scale using PyTorch or JAX.
• Demonstrated research productivity with publications, reports, presentations, or open-source software.

Responsibilities

• Develop and evaluate methods for interpreting internal representations of deep models, including sparse decompositions and concept extraction.
• Design human-in-the-loop workflows for domain experts to explore and validate concepts.
• Establish rigorous evaluation methodology for interpretability claims (faithfulness, stability, causal grounding).
• Research, design, implement, and apply advanced ML methods for applications in a collaborative scientific environment.
• Contribute to grant proposals and collaborate with multidisciplinary teams.

Skills

Explainable AI

Deep Learning

Python

Research Publications

Education

PhD in Computer Science/ML/Applied Math/Statistics/HCI

Tools

PyTorch

JAX

Python

Job description

Explainable AI - Postdoctoral Researcher

Mid-Senior Level | Full-time
Postdoctoral/Fellowship | Livermore, CA | 08/12/2026
Reference #: REF8682W
Job Code: PDS.1 Post-Dr Research Staff 1
Organization: Computing
Position Type: Post Doctoral
Security Clearance: None/Position does not require US citizenship (assignments longer than 179 days require a federal background investigation)
Drug Test: Required for external applicant(s) selected for this position (includes testing for use of marijuana)
Medical Exam: Not applicable

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.

Job Description

We have an opening for a Postdoctoral Researcher 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. Your work will focus on recovering human-meaningful structure from learned representations, including sparse decompositions of activations, concept discovery, mechanistic analysis, and causal intervention, and on the interactive interfaces and evaluation methodology that let experts interrogate that structure and the given explanations. Applications area includes but not limited to multimodalSciMLmodels and deep surrogates for various simulations. This position will be in the Machine Intelligence Group in the Center for Applied Scientific Computing (CASC) Division within the LLNL Computing Directorate.

This position offers a hybrid schedule, blending in-person and virtual presence. You will have the flexibility to work from home one or more days per week.

• Develop and evaluate methods for interpreting the internal representations of deep models, including sparse decompositions of activations, concept extraction, and representation steering.
• 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.
• Conduct cutting-edge machine learning research effectively and independently.
• Actively participate with project scientists and engineers in defining, planning, and formulating experimental, modeling, and simulation efforts for complex problems stemming from national security applications.
• Propose and implement advanced analysis methodologies, collect and analyze data, and document results in technical reports and peer-reviewed publications.
• Contribute to grant proposals and collaborate with others in a multidisciplinary team environment, including academic and industrial partners, to accomplish research goals.
• Pursue independent (but complementary) research interests and interact with a broad spectrum of scientists internal and external to the Laboratory.
• Perform other duties as assigned.

Qualifications

• Recent Ph.D. in Computer Science, Machine Learning, Applied Mathematics, Statistics, Human-Computer Interaction, or a related field.
• 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.

Desired Qualifications

• Experience with sparse autoencoders, transcoders, or related feature-learning methods applied to the activations of large pretrained models.
• 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.
• Experience connecting interpretability to uncertainty quantification, robustness, calibration, or AI safety and assurance evaluation.
• Experience with high-performance computing, GPU programming, parallel programming, cloud computing, and/or related methods including running numerical simulations of complex workflows.
• Demonstrated technical leadership in fields related to machine learning, such as mentorship or managing teams.
• Experience or interest in scientific applications, such as, material science, climate science, etc.

Pay Range

$143,328 Annually

Additional Information

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 2026Best Places to Work by Glassdoor!
• Flexible schedules (*depending on project needs)

We are an equal opportunity employer that is committed to providing all with a work environment free of discrimination and harassment. All qualified applicants will receive consideration for employment without regard to race, color, religion, marital status, national origin, ancestry, sex, sexual orientation, gender identity, disability, medical condition, pregnancy, protected veteran status, age, citizenship, or any other characteristic protected by applicable laws.

Reasonable Accommodation

Our goal is to create an accessible and inclusive experience for all candidates applying and interviewing at the Laboratory. If you need a reasonable accommodation during the application or the recruiting process, please use our online form to submit a request.

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