Computational Scientist - AI/ML Engineer for Climate Science

In A Nutshell Location : Hybrid Chicago, IL, USA Salary : $85,000-$105,000 Job Type : Full-time Experience Level : Entry-level Deadline to apply : July 16, 2026 Support faculty, postdoctoral researchers, and graduate students conducting computational and AI-driven research. 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. Skillset 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. 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. #J-18808-Ljbffr datadotorg