Staff Scientist - AI for Science Staff Scientist - AI for Science Argonne National Laboratory H[...]

The Argonne Leadership Computing Facility (ALCF) is seeking a Staff Scientist in AI for Science at Scale to help drive the next generation of scientific discovery using advanced AI, high-performance computing, and emerging computing architectures. This is an opportunity to work at the frontier of AI for science and on the Department of Energy Genesis Mission, where novel machine learning methods, large-scale scientific data, and leadership-class supercomputers come together to accelerate discovery in fields such as physics, materials science, chemistry, biology, climate, energy, and beyond. The successful candidate will develop and optimize scientific machine learning methods and applications on cutting‑edge supercomputers and novel AI systems, while also helping shape future computing platforms designed to meet the needs of AI‑enabled science. You will join the AI group – a highly collaborative, multidisciplinary environment and work alongside experts in AI, simulation, computer science, applied mathematics, and domain science. This role offers the chance to contribute both foundational advances and real‑world scientific outcomes, with opportunities to publish in leading journals and conferences, engage with national and international collaborators, and influence the future of AI and HPC for scientific research. In this role, you will: Conduct research and development aligned with Argonne’s strategic mission in computation, AI, and scientific discovery. Develop, scale, and optimize AI and scientific machine learning methods for leadership‑class supercomputers and emerging AI‑oriented architectures. Advance the use of AI for science at scale, including workflows that integrate simulation, data, and machine learning. Contribute to the design and evaluation of future supercomputing systems to ensure they meet the demands of AI‑enabled scientific applications. Partner with computational scientists, applied mathematicians, and domain researchers to solve challenging scientific problems with high impact. Address algorithmic, systems, and data challenges associated with high‑performance scientific machine learning, including performance, scalability, and usability. Conduct original research in computational science and leadership computing, and communicate findings through publications, conference presentations, software, reports, and other research outputs. Build and strengthen an independent research portfolio through new technical directions, collaborations, and professional visibility. 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. This position qualifies as “Hybrid Remote Work – Mostly Onsite”: employees are regularly scheduled for some onsite and some remote days, with employees typically working up to 40% of their time remotely. Position Requirements RD2 : Bachelor's degree and 5+ years of experience, or a Master's and 3+ years of experience, or a PhD and 0+ years of experience, or equivalent. Education in computer science, applied mathematics, statistics, computational science, or a related field. Demonstrated advanced knowledge of one or more of machine learning, data mining, and statistics. Strong background in mathematical optimization or linear algebra. Advanced knowledge and significant experience in one or more programming languages such as Python, C, or C++. Significant experience with machine learning toolkits such as PyTorch or JAX. Experience with distributed training and post‑training frameworks and scaling is very desirable. Effective verbal and written communication skills. Software development practices and techniques for computational science problems. Experience and skills in interdisciplinary research involving mathematicians, computer scientists, and application scientists. Ability to model Argonne’s core values of impact, safety, respect, integrity, and teamwork. Job Family Research Development (RD) Job Profile Computer Science 2 Worker Type Regular Time Type Full time The expected hiring range for this position is $94,486.00 – $147,398.94. The pay offered to a selected candidate will be determined based on factors such as 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. 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. Argonne employees, and certain guest researchers and contractors, are subject to particular restrictions related to participation in Foreign Government Sponsored or Affiliated Activities, as defined and detailed in United States Department of Energy Order 486.1A. You will be asked to disclose any such participation in the application phase for review by Argonne's Legal Department. 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. Please be advised that Argonne positions require upon hire (or may require in the future) for the individual to obtain a government access authorization that involves additional background check requirements. Failure to obtain or maintain such government access authorization could result in the withdrawal of a job offer or future termination of employment. #J-18808-Ljbffr Seeds Renewables