Postdoctoral Researcher, Explainable AI

SmartRecruiters, Inc.; Livermore (CA); Hybrid; USD 122,000 - 165,000; Full time

Benefits offered by this job

Hybrid work model

Job summary

Lawrence Livermore National Laboratory invites applications for a Postdoctoral Researcher in Explainable AI. The role focuses on recovering human-meaningful structure from learned representations, including sparse activations, concept discovery, mechanistic analysis, and causal intervention, within a hybrid in-person/remote setting in the ML group of CASC.

You will develop interpretable methods for large models, design human-in-the-loop workflows, and contribute to peer-reviewed publications and

Qualifications

• Recent Ph.D. in Computer Science, Machine Learning, Applied Mathematics, Statistics, HCI, or related field.
• In-depth knowledge in explainable AI and related topics with publications.
• Experience with explainable/interpretable AI, representation learning, mechanistic interpretability or visual analytics.
• Experience with PyTorch or JAX and deep learning at medium to large scale.
• Proven research productivity with publications, reports, or open-source software.

Responsibilities

• Develop and evaluate methods for interpreting internal representations of deep models.
• Design human-in-the-loop workflows for domain experts to explore concepts.
• Establish evaluation methodology for interpretability claims (faithfulness, stability, causal grounding).
• Conduct ML research and apply methods to national security-relevant applications.
• Collaborate with scientists and engineers in planning experimental and simulation efforts.

Skills

Explainable AI

Deep learning

PyTorch/JAX

Research publications

Education

Recent Ph.D. in CS/ML/Math/Stats

Tools

Python ecosystem

PyTorch

JAX

High-performance computing

Job description

Explainable AI - Postdoctoral Researcher

• Full-time
• Organization: Computing
• Category: Postdoctoral/Fellowship
• Employee Referral Bonus: Not applicable
• Job Code 1: PDS.1 Post-Dr Research Staff 1
• Pre-Employment Drug Test: Required for external applicant(s) selected for this position (includes testing for use of marijuana)
• Pre-Placement Medical Exam: Not applicable
• Security Clearance: None/Position does not require US citizenship (assignments longer than 179 days require a federal background investigation)
• Position Type: Post Doctoral

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 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.
  • 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.

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.

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)

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.

Wireless and Medical Devices

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

Ifyou useamedical device, whichpairs with a mobile device,you must still follow the rules concerningthe mobile device in individual sections within Limited Areas. Sensitive Compartmented Information Facilities requireseparate 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.

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.

CaliforniaPrivacy Notice

The California Consumer Privacy Act (CCPA) grants privacy rights to all California residents. The law also entitlesjob applicants, employees, and non-employee workers to be notified of what personal information LLNL collects and for what purpose. The Employee Privacy Notice can be accessed here .