Explainable AI - Postdoctoral Researcher

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.

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 multimodal SciML models 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. 

Essential Duties

• 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.

• 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 

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 Benefits Package
• 401(k)
• Relocation Assistance
• Education Reimbursement Program
• Flexible schedules (*depending on project needs)
• Our values - visit https://www.llnl.gov/inclusion/our-values

Security Clearance

None required.  However, if your assignment is longer than 179 days cumulatively within a calendar year, you must go through the Personal Identity Verification process.  This process includes completing an online background investigation form and receiving approval of the background check.  

National Defense Authorization Act (NDAA)

The 2025 National Defense Authorization Act (NDAA), Section 3112, generally prohibits citizens of China, Russia, Iran and North Korea without dual US citizenship or legal permanent residence from accessing specific non-public areas of national security or nuclear weapons facilities.  The restrictions of NDAA Section 3112 apply to this position.  To be qualified for this position, Candidates must be eligible to access the Laboratory in compliance with Section 3112.

Pre-Employment Drug Test

External applicant(s) selected for this position must pass a post-offer, pre-employment drug test. This includes testing for use of marijuana as Federal Law applies to us as a Federal Contractor.

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Per the Department of Energy (DOE), Lawrence Livermore National Laboratory must meet certain restrictions with the use and/or possession of mobile devices in Limited Areas. Depending on your job duties, you may be required to work in a Limited Area where you are not permitted to have a personal and/or laboratory mobile device in your possession.  This includes, but not limited to cell phones, tablets, fitness devices, wireless headphones, and other Bluetooth/wireless enabled devices.  

If you use a medical device, which pairs with a mobile device, you must still follow the rules concerning the mobile device in individual sections within Limited Areas.  Sensitive Compartmented Information Facilities require separate approval. Hearing aids without wireless capabilities or wireless that has been disabled are allowed in Limited Areas, Secure Space and Transit/Buffer Space within buildings.

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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.

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