Staff Scientist: Post-Training & RL for AI in Science

Argonne National Laboratory; United States; Hybrid; USD 94,000 - 147,000; Full time

Benefits offered by this job

Comprehensive benefits

Job summary

Argonne National Laboratory seeks a Staff Scientist in Post-Training and Reinforcement Learning for AI for Science to advance foundation models and learning systems for scientific discovery at leadership-class supercomputers.

You will develop, scale, and evaluate post-training methods including reinforcement learning and alignment techniques for scientific AI models and workflows, collaborating across AI, simulation, and domain science while contributing to high-impact publications and

Qualifications

• Bachelor's degree or higher in CS or related field.
• Advanced knowledge in 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.
• Programming experience in Python, C, or C++.
• Experience with ML frameworks such as PyTorch or JAX.
• Experience with large-scale training and post-training workflows.
• Ability to work in interdisciplinary teams.

Responsibilities

• Conduct research and development aligned with Argonne's mission in computation, AI, and scientific discovery.
• Develop, scale, and optimize post-training methods for scientific foundation models, including RL, preference-based optimization, fine-tuning, alignment.
• Advance techniques to improve performance, controllability, and reliability of AI models for science.
• Design and evaluate methods for applying RL and post-training pipelines to large-scale environments.
• Develop and optimize workflows for training on leadership-class supercomputers and AI-oriented architectures.
• Collaborate with computational scientists and domain researchers on challenging problems.
• Address algorithmic, systems, and data challenges with large-scale training and post-training.

Skills

Python

C/C++

Machine Learning / Reinforcement

Optimization

Distributed training

Communication skills

Interdisciplinary collaboration

Education

Bachelor's degree

Master's degree

PhD

Related field degree

Tools

PyTorch

JAX

TensorFlow

Linux

Job description

Argonne National Laboratory seeks a Staff Scientist in Post-Training and Reinforcement Learning for AI for Science to advance foundation models and learning systems for scientific discovery at leadership-class supercomputers.

You will develop, scale, and evaluate post-training methods including reinforcement learning and alignment techniques for scientific AI models and workflows, collaborating across AI, simulation, and domain science while contributing to high-impact publications and