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

Argonne National Laboratory; Lemont (IL); Hybrid; USD 94,486 - 147,398; Full time

Job summary

Argonne National Laboratory in Lemont, Illinois, is seeking a Staff Scientist to develop, scale, and evaluate post-training methods for AI models. The role involves interdisciplinary collaboration and aims to advance machine learning techniques for scientific applications.

The ideal candidate will have a strong foundation in machine learning, programming, and mathematical optimization, and will contribute to research that impacts the AI for science mission.

Qualifications

• 5+ years of experience with a Bachelor's; 3+ years with a Master's; or PhD.
• Advanced knowledge in machine learning and statistics is essential.
• Strong background in mathematical optimization and numerical methods.

Responsibilities

• Conduct research aligned with computational AI and scientific discovery.
• Develop post-training methods for scientific foundation models.
• Design methods for applying reinforcement learning to large-scale environments.

Skills

Machine learning

Reinforcement learning

Large-scale model training

Python

C

C++

Mathematical optimization

Education

Bachelor’s degree or Master's or PhD in relevant fields

Tools

PyTorch

JAX

Job description

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

The Argonne Leadership Computing Facility (ALCF) seeks a Staff Scientist who will develop, scale, and evaluate post‑training methods, including reinforcement learning and preference optimization, for scientific AI models.

Role Focus: Contribute fundamental advances in machine learning and high‑impact scientific applications within a multidisciplinary team of AI, simulation, computer science, applied mathematics, and domain scientists.

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, and alignment.
• Advance techniques that improve the performance, controllability, reliability, and scientific utility of AI models for scientific 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

• Bachelor’s degree with 5+ years of experience; or Master’s with 3+ years; or PhD (or equivalent) in computer science, applied mathematics, statistics, computational science, or a 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.
• 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.

Working Arrangement

Hybrid Remote Work – Mostly Onsite (employees regularly scheduled for some onsite and some remote days, typically working up to 40% of their time remotely).

Compensation

The expected hiring range for this position is $94,486.00 – $147,398.94. Benefits are part of the total rewards package.

Employment Commitment

All Argonne offers of employment are contingent upon a background check that includes an assessment of criminal conviction history conducted on an individualized and case‑by‑case basis. Positions may require a government access authorization. Failure to obtain or maintain such authorization could result in withdrawal of the job offer or future termination of employment.

Equal Employment Opportunity

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 and considers all qualified applicants for employment without regard to any characteristic protected by law.