Machine Learning - Postdoctoral Researcher

LLNL • Livermore, CA

3d • Hybrid

About The Position

Lawrence Livermore National Laboratory is seeking a Machine Learning Postdoctoral Researcher to conduct fundamental research and development in machine learning and statistical methods across projects in safety and security, foundation models for areas such as materials science or bioassurance, and uncertainty quantification for deep learning. The interdisciplinary work combines state-of-the-art machine learning with scientific objectives, with examples including multimodal sequence-to-sequence models for molecules and chemical reactions and combining large language models with other modalities. The researcher will also develop methods to improve model safety and trustworthiness.

The role is in the Machine Intelligence Group in the Center for Applied Scientific Computing (CASC) Division of the LLNL Computing Directorate. The postdoctoral appointment is a temporary, mentored training position for researchers early in their postdoctoral careers, with the possibility of extension up to a maximum of three years. Applicants must have received their Ph.D. or equivalent doctorate within five years of the anticipated start date and must be eligible to access the Laboratory under Section 3112 of the National Defense Authorization Act.

The position involves collaborative research with project scientists and engineers, as well as independent but complementary research and interaction with scientists inside and outside the Laboratory. The salary range begins at $138,480 annually; placement in the range depends on factors including qualifications, experience, skills, location, and organizational needs.

Requirements

• Must be eligible to access the Laboratory in compliance with Section 3112 of the National Defense Authorization Act.
• Recent Ph.D. in Machine Learning, Optimization, Computer Science, Mathematics, or a related field.
• Must have received a Ph.D. or equivalent doctorate within five years of the anticipated start date.
• Demonstrated ability and desire to develop substantial domain knowledge in application fields, communicate effectively with subject-matter experts, and identify 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 or TensorFlow, demonstrated through medium- to large-scale deep learning models and experiments.
• Demonstrated research productivity, documented through publications, reports, presentations, and/or open-source software in relevant venues.
• Experience with scientific programming in the Python ecosystem, demonstrated through software artifacts such as deep learning models, workflows, or simulations.
• Experience in one or more deep-learning areas: large language models, graph neural networks, multimodal models, generative models, robustness, or explainable AI.

Nice To Haves

• Experience with high-performance computing, GPU programming, parallel programming, cloud computing, related methods, or numerical simulations of complex workflows.
• Demonstrated technical leadership in machine-learning-related fields, such as mentoring or managing teams.
• Experience or interest in scientific applications such as materials science or climate science.

Responsibilities

• Research, design, implement, and apply advanced machine learning methods for multiple applications in a collaborative scientific environment.
• Participate with project scientists and engineers in defining, planning, and formulating experimental, modeling, and simulation efforts for complex national security problems.
• Propose and implement advanced analysis methodologies, collect and analyze data, and document results in technical reports and peer-reviewed publications.
• Contribute to grant proposals.
• Collaborate with multidisciplinary teams, including academic and industrial partners, to accomplish research goals.
• Pursue independent but complementary research interests and interact with scientists inside and outside the Laboratory.
• Perform other duties as assigned.

Benefits

• Flexible benefits package
• 401(k)
• Relocation assistance
• Education reimbursement program
• Flexible schedules, depending on project needs

What This Job Offers

• Job Type: Full-time
• Career Level: Entry Level
• Education Level: Ph.D. or professional degree