Computational Scientist – AI/ML Engineer for Climate Science

About The Department

The 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.

Job Summary

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.

Responsibilities

• Support 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.
• Auxiliary researchers with compiling, debugging, profiling, tuning, and porting scientific applications.
• 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.

Education and Experience

• Minimum requirements include a college or university degree in related field.
• 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.
• Familiarity with Linux/UNIX environments and HPC systems.
• 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 scaling, and performance optimization for AI/ML applications.
• Familiarity with scientific computing libraries such as NumPy, SciPy, pandas, xarray, and scikit-learn.
• Experience working with large-scale scientific datasets and formats such as NetCDF and HDF5.
• Experience applying AI/ML methods to climate, atmospheric, or earth system science problems.
• Experience with climate and community modeling frameworks such as WRF or CESM.
• Experience with container technologies and development tools such as Git and Docker.
• Experience installing, optimizing, and profiling scientific software on HPC systems.
• Familiarity with performance analysis and compiler optimization techniques.
• Experience with distributed and parallel computing technologies such as MPI and OpenMP.
• Experience with large-scale neural network architectures for processing spatiotemporal data, such as Vision Transformers (ViTs).
• Experience with generative modeling with deep learning, such as flow matching or stochastic interpolants.

Preferred Competencies

• Understand and translate researchers' scientific goals into computational requirements.
• Work well with faculty and researchers.
• Identify and gain expertise in appropriate new technologies and/or software tools.
• Function as part of an interactive team while demonstrating self-initiative to achieve project's goals and Research Computing Center's mission.
• Strong analytical skills and problem-solving ability.