Computational Scientist – AI/ML Engineer for Climate Science

University of Chicago•Austin, TX

7d•$85,000 - $105,000•Hybrid

About The Position

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. The Office of Research oversees sponsored research administration, research development, and contract management across the University. 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, and the University of Chicago Data Intelligence Lab (DIL), 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.

Requirements

Minimum requirements include a college or university degree in related field.
Minimum requirements include knowledge and skills developed through 2-5 years of work experience in a related job discipline.
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 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.
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.

Nice To Haves

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.
Minimum of two years of relevant research or professional experience in AI/ML, scientific computing, climate science, atmospheric science, or related computational research environments.

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.
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.