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

Job Overview

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.

This role sits at the frontier of AI for science and the Department of Energy Genesis mission, where large-scale machine learning, scientific data, simulation, and leadership‑class supercomputers combine to enable new modes of discovery across physics, materials science, chemistry, biology, climate, energy, and related fields. The successful candidate will conduct research on methods that improve the usefulness, reliability, and scientific performance of large‑scale AI models after pretraining, and will advance the systems and software needed to run these methods efficiently on cutting‑edge supercomputers and emerging AI platforms.

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.

• Work closely with colleagues across national laboratories, universities, industry, and supercomputing centers on current and future systems for the AI for science mission.

• Contribute to a team culture that values scientific excellence, collaboration, innovation, and inclusive professional growth.

Position Requirements

Required Qualifications

• RD2: Bachelor’s degree and 5+ years of experience, or a Master’s 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 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 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.

Work Arrangement

This position is Hybrid Remote Work – Mostly Onsite, with employees scheduled for some onsite and some remote days, typically up to 40% of the time remotely.

Compensation

Pay range: $94,486.00 - $147,398.94. The offer will be based on qualifications and experience.

Benefits

Comprehensive benefits are part of the total rewards package.

Equal Employment Opportunity

As an equal employment opportunity employer, 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 and is committed to nondiscrimination and considers all qualified applicants for employment without regard to any characteristic protected by law.