Computational Scientist

Computational Scientist – AI/ML EngineerThe 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.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.Develops and presents technical training materials and web-based documentation. Ensures timely systems support and updates. Assists in conducting information security assessments and risk analysis of computing environment.Evaluates past and present technologies to help develop new tools. Ensures all the new tools have been through quality control reviews.Performs other related work as needed.Minimum QualificationsEducation:Minimum requirements include a college or university degree in related field.Work Experience:Minimum requirements include knowledge and skills developed through 2-5 years of work experience in a related job discipline.Certifications:---Preferred QualificationsEducation:PhD in Computer Science, Applied Mathematics, Atmospheric Science, Physics, Earth System Science, or a related field with a strong AI/ML or computational science focus.Experience:Minimum of two years of relevant research or professional experience in AI/ML, scientific computing, climate science, atmospheric science, or related computational research environments.Technical Skills and Knowledge:Strong programming skills in Python and/or C++.Experience with AI/ML frameworks such as PyTorch or TensorFlow.Experience developing, training, and optimizing neural network and deep learning architectures.Experience with Linux/UNIX environments and HPC systems.Familiarity with job schedulers such as SLURM.Experience deploying and optimizing workloads on GPU-accelerated systems.Familiarity with climate, weather, atmospheric, or Earth system data workflows and computational challenges.Understanding of distributed training, model s