Computational Scientist - AI​/ML Engineer Climate Science

The University of Chicago seeks a Computational Scientist - AI/ML Engineer for its Climate Science Department. This role involves developing software for data acquisition and integration, optimizing AI/ML workflows, and supporting climate research projects. The position requires collaboration with researchers and includes user engagement and training. It is a hybrid role, requiring at least three days onsite each week, based in Austin, TX.
Position: Computational Scientist - AI/ML Engineer for Climate Science DepartmentProvost Research Computing CenterAbout the DepartmentThe University of Chicago Research Computing Center (RCC), a unit within the Office of Research, provides advanced research computing resources and expertise to support computational and data‑intensive research across the University. RCC enables research through centrally managed high‑performance computing (HPC), storage, visualization, and AI infrastructure, along with scientific consulting, user support, education, and training. RCC also helps researchers leverage local, national, and cloud‑based computational resources.The Office of Research oversees sponsored research administration, research development, and contract management across the University.Job Summary The job develops software to support the data acquisition, ingestion, and integration for research projects. Assists in the development of user interfaces and scalable back‑end services to automate and accelerate the scientific output of multi‑institutional research projects.The Research Computing Center (RCC) seeks an experienced Computational Scientist - AI/ML Engineer to support faculty, postdoctoral researchers, and graduate students conducting computational and AI‑driven research. This position will contribute to a major new AI and climate computing initiative in collaboration with NVIDIA, the University of Chicago Data Science Institute (DSI), Argonne National Laboratory, University of Chicago Development Innovation Lab (DIL), AI for Climate (AICE), and Human‑Centered Weather Forecasts (HCWF) supporting next‑generation climate and Earth system AI research and infrastructure development.The successful candidate will collaborate closely with researchers to understand scientific challenges, develop and optimize AI/ML workflows, neural networks, and deploy scalable solutions on modern HPC and GPU‑accelerated systems. This role includes supporting climate and geophysical science applications, enabling large‑scale AI training and inference workflows, and contributing to the advancement of AI‑enabled scientific discovery.The ideal candidate will have experience working at the intersection of AI/ML, climate science, and large‑scale scientific computing environments.As part of RCC's Computational Scientist team, the candidate will also contribute to user engagement, training, documentation, and grant support activities that advance computational research at the University of Chicago.This is a hybrid position requiring at least three days onsite per week.ResponsibilitiesSupport computational applications, software, and workflows related to climate, atmospheric, geophysical, and earth system sciences.Collaborate with researchers to translate scientific challenges into scalable AI/ML and computational solutions.Deploy, optimize, and support AI/ML pipelines on HPC and GPU‑accelerated systems.Optimize large‑scale training and inference workflows using distributed computing frameworks and performance analysis tools such as NVIDIA Nsight.Assist researchers with compiling, debugging, profiling, tuning, and porting scientific applications.Optimize system utilization, including CPU/GPU, memory, storage, and I/O performance.Maintain and support scientific software environments, community codes, and research datasets relevant to climate and earth system science.Consult with faculty and research groups to help them effectively utilize RCC, national computing facilities, and cloud resources.Contribute technical expertise to grant proposals and collaborative research initiatives.Stay informed on emerging AI methods, climate modeling advances, and GPU computing technologies relevant to Earth system science.Develop and present technical training materials and web‑based documentation. Ensure timely systems support and updates. Assist in conducting information security assessments and risk analysis of computing environment.Evaluate past and present technologies to help develop new tools. Ensure all the new tools have been through quality control reviews.Perform other related work as needed.Minimum Qualifications Education: Minimum requirements include a college or university degree in…