Machine Learning (AI Safety & Security) - Postdoctoral Researcher

Company DescriptionJoin us and make YOUR mark on the World!Are you interested in joining some of the brightest talent in the world to strengthen the United States' security? Come join Lawrence Livermore National Laboratory (LLNL) where our employees apply their expertise to create solutions for BIG ideas that make our world a better place.We are committed to a diverse and equitable workforce with an inclusive culture that values and celebrates the diversity of our people, talents, ideas, experiences, and perspectives. This is important for continued success of the Laboratory's mission.Pay Range$8,850 - $11,040 MonthlyPlease note that the pay range information is a general guideline only. Many factors are taken into consideration when setting starting pay including education, experience, the external labor market, and internal equity.Job DescriptionWe have openings for Postdoctoral Research Staff Member 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 bioassurance, 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• 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 in order to communicate effectively 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.Additional InformationAll your information will be kept confidential according to EEO guidelines.Position InformationThis 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 2024 Best Places to Work by Glassdoor!• Flexible Benefits Package• 401(k)• Relocation Assistance• Education Reimbursement Program• Flexible schedules (*depending on project needs)• Inclusion, Diversity, Equity and Accountability (IDEA) - visit https://www.llnl.gov/diversity• Our core beliefs - visit https://www.llnl.gov/diversity/our-values• Employee engagement - visit https://www.llnl.gov/diversity/employee-engagementSecurity ClearanceThis position requires either no security clearance, or a Department of Energy (DOE) L-level or Q-level clearance depending on the particular assignment.If you are selected and a security clearance is required, we will initiate a Federal background investigation to determine if you meet eligibility requirements for access to classified information or matter. Also, all L or Q cleared employees are subject to random drug testing. L and Q-level clearances require U.S. citizenship.If no security clearance is required, but 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. (This process does not apply to foreign nationals.)Pre-Employment Drug TestExternal 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 DevicesPer 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 advertisementsPlease be aware of recruitment scams where people or entities are misusing the name of Lawrence Livermore National Laboratory (LLNL) to post fake job advertisements. 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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.We invite you to review the Equal Employment Opportunity posters which include EEO is the Law and Pay Transparency Nondiscrimination Provision.Reasonable AccommodationOur 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 NoticeThe California Consumer Privacy Act (CCPA) grants privacy rights to all California residents. 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