Staff Scientist – Post-Training and Reinforcement Learning for AI for Science

About the role

The ALCF is seeking a Staff Scientist in Post-Training and Reinforcement Learning for AI for Science to advance the next generation of foundation models and learning systems for scientific discovery. This role combines fundamental advances in machine learning with impactful scientific applications.

Responsibilities

• Conduct research and development aligned with Argonne’s strategic mission in computation, AI, and scientific discovery.
• Develop, scale, and optimize post-training methods for scientific foundation models, including reinforcement learning, preference-based optimization, fine-tuning, alignment, and related approaches.
• Advance techniques that improve the performance, controllability, reliability, and scientific utility of AI models for science applications.
• Design and evaluate methods for applying reinforcement learning and post-training pipelines to large-scale scientific and data-intensive environments.
• Develop and optimize workflows for training and post-training on leadership-class supercomputers and emerging AI-oriented architectures.
• Partner with computational scientists, applied mathematicians, and domain researchers to apply foundation models and adaptive learning systems to challenging scientific problems with high impact.
• Address algorithmic, systems, and data challenges associated with large-scale training and post-training, including performance, scalability, robustness, and usability.
• Conduct original research in computational science and AI at scale, and communicate findings through publications, conference presentations, software, reports, and other research outputs.
• Contribute to a team culture that values scientific excellence, collaboration, innovation, and inclusive professional growth.

Requirements

• Bachelor's degree and 5+ years of experience, or a Masters and 3+ years of experience, or a PhD, or equivalent Education in computer science, applied mathematics, statistics, computational science, or a related field.
• Demonstrated advanced knowledge in one or more of the following areas: machine learning, reinforcement learning, large-scale model training, post-training, optimization, data mining, or statistics.
• Strong background in mathematical optimization, linear algebra, or numerical methods.
• Advanced knowledge of and significant programming experience in one or more languages such as Python, C, or C++.
• Significant experience with machine learning frameworks such as PyTorch or JAX.
• Experience with large-scale training, distributed learning systems, or post-training workflows.
• Experience with software development practices and techniques for computational science and machine learning systems.
• Able to work effectively in interdisciplinary teams involving mathematicians, computer scientists, and application scientists.
• Effective written and verbal communication skills.
• Ability to model Argonne’s core values of impact, safety, respect, integrity, and teamwork.