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

The Argonne Leadership Computing Facility (ALCF) is seeking a Staff Scientist in Post-Training and Reinforcement Learning for AI for Science to help advance the next generation of foundation models and learning systems for scientific discovery.

Required Qualifications:

• RD2: 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
• Ability 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

Preferred Qualifications:

• Experience with reinforcement learning, policy optimization, bandits, preference learning, or related methods
• Experience with post-training methods for large models, including supervised fine-tuning, reinforcement learning from feedback, direct preference optimization, reward modeling, or model adaptation
• Experience with distributed training, large-scale optimization, and multi-node or multi-accelerator execution

Please note that the pay range information is a general guideline only. The pay offered to a selected candidate will be determined based on factors such as, but not limited to, the scope and responsibilities of the position, the qualifications of the selected candidate, business considerations, internal equity, and external market pay for comparable jobs. Additionally, comprehensive benefits are part of the total rewards package.

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As an equal employment opportunity employer, and in accordance with our core values of impact, safety, respect, integrity and teamwork, Argonne National Laboratory is committed to a safe and welcoming workplace that fosters collaborative scientific discovery and innovation. Argonne encourages everyone to apply for employment. Argonne is committed to nondiscrimination and considers all qualified applicants for employment without regard to any characteristic protected by law.