Asks for

The ad says“Experience with one or more of the following areas of deep learning: large language models, graph neural networks, multimodal models, generative models, robustness, explainable AI”
The ad says“Experience with one or more of the following areas of deep learning: large language models, graph neural networks, multimodal models, generative models, robustness, explainable AI”
The ad says“Experience with one or more of the following areas of deep learning: large language models, graph neural networks, multimodal models, generative models, robustness, explainable AI”
The ad says“Experience with one or more of the following areas of deep learning: large language models, graph neural networks, multimodal models, generative models, robustness, explainable AI”
The ad says“We're looking for a Machine Learning Postdoctoral Researcher to contribute to fundamental R&D in machine learning and statistical methods in support of different projects related to AI Safety & Security, Foundation Models in areas such as material science or bio assurance, and uncertainty quantification for deep learning models. These will be interdisciplinary projects that aim to combine state-of-the-art machine learning models with various science objectives. Examples are multi-modal sequence-to-sequence models for molecules and chemical reactions or combine large language models with other modalities. Furthermore, you will develop methods to improve safety and trustworthiness of these models. This position will be in the Machine Intelligence Group in the Center for Applied Scientific Computing (CASC) Division within the LLNL Computing Directorate.”
The ad says“Experience with one or more of the following areas of deep learning: large language models, graph neural networks, multimodal models, generative models, robustness, explainable AI”
The ad says“Experience with scientific programming in the Python ecosystem as evidence through software artifacts, such as deep learning models, workflows, simulations, or similar”
The ad says“Experience developing, implementing and applying advanced statistical or machine learning models and algorithms using modern software libraries such as PyTorch, TensorFlow, or similar as evidence through medium to large scale deep learning models and experiments.”
The ad says“Experience with one or more of the following areas of deep learning: large language models, graph neural networks, multimodal models, generative models, robustness, explainable AI”
The ad says“We're looking for a Machine Learning Postdoctoral Researcher to contribute to fundamental R&D in machine learning and statistical methods in support of different projects related to AI Safety & Security, Foundation Models in areas such as material science or bio assurance, and uncertainty quantification for deep learning models. These will be interdisciplinary projects that aim to combine state-of-the-art machine learning models with various science objectives. Examples are multi-modal sequence-to-sequence models for molecules and chemical reactions or combine large language models with other modalities. Furthermore, you will develop methods to improve safety and trustworthiness of these models. This position will be in the Machine Intelligence Group in the Center for Applied Scientific Computing (CASC) Division within the LLNL Computing Directorate.”
The ad says“Experience developing, implementing and applying advanced statistical or machine learning models and algorithms using modern software libraries such as PyTorch, TensorFlow, or similar as evidence through medium to large scale deep learning models and experiments.”

Company Description

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're looking for a Machine Learning Postdoctoral Researcher to contribute to fundamental R&D in machine learning and statistical methods in support of different projects related to AI Safety & Security, Foundation Models in areas such as material science or bio assurance, and uncertainty quantification for deep learning models. These will be interdisciplinary projects that aim to combine state-of-the-art machine learning models with various science objectives. Examples are multi-modal sequence-to-sequence models for molecules and chemical reactions or combine large language models with other modalities. Furthermore, you will develop methods to improve safety and trustworthiness of these models. This position will be in the Machine Intelligence Group in the Center for Applied Scientific Computing (CASC) Division within the LLNL Computing Directorate.

You will

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

Must be eligible to access the Laboratory in compliance with Section 3112 of the National Defense Authorization Act (NDAA). See Additional Information section below for details.
Recent Ph.D. in Machine Learning, Optimization, Computer Science, Mathematics or a related field.
Demonstrated ability and desire to obtain substantial domain knowledge in fields of application to enable effective communication with subject matter experts, and to identify novel, impactful applications of machine learning.
Experience developing, implementing and applying advanced statistical or machine learning models and algorithms using modern software libraries such as PyTorch, TensorFlow, or similar as evidence through medium to large scale deep learning models and experiments.
Demonstrated research productivity, as documented by publications, reports, presentations, and/or open-source software in relevant venues (NeurIPS, ICML, ICLR, CVPR, AAAI, AISTATS, UAI, KDD, JMLR, Nature etc.)
Experience with scientific programming in the Python ecosystem as evidence through software artifacts, such as deep learning models, workflows, simulations, or similar
Experience with one or more of the following areas of deep learning: large language models, graph neural networks, multimodal models, generative models, robustness, explainable AI

Qualifications We Desire

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

$138,480 Annually

This is the lowest to highest salary range in good faith we would pay for this role at the time of this posting. An employee’s position within the salary range will be based on several factors including, but not limited to, specific competencies, relevant education, qualifications, certifications, experience, skills, seniority, geographic location, performance, and business or organizational needs.

Additional Information

#LI-Hybrid

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.

Wireless and Medical Devices

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.

How to identify fake job advertisements

Please be aware of recruitment scams where people or entities are misusing the name of Lawrence Livermore National Laboratory (LLNL) to post fake job advertisements. LLNL never extends an offer without a personal interview and will never charge a fee for joining our company. All current job openings are displayed on the Career Page under “Find Your Job” of our website. If you have encountered a job posting or have been approached with a job offer that you suspect may be fraudulent, we strongly recommend you do not respond.

To learn more about recruitment scams: https://www.llnl.gov/sites/www/files/2023-05/LLNL-Job-Fraud-Statement-Updated-4.26.23.pdf

Equal Employment Opportunity

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.

California Privacy Notice

The California Consumer Privacy Act (CCPA) grants privacy rights to all California residents. The law also entitles job 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.