Computational Scientist- HPC/AI Generalist

University of Chicago•Hyde Park, IL

9d•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) is seeking a highly motivated Computational Scientist to work closely with faculty and researchers at The University of Chicago. The person in this position will serve as a multi-disciplinary technical expert in supporting and advising faculty on using High-Performance Computing (HPC), AI, and related technologies for their research. The successful candidate will join a team of Computational Scientists who are playing a key role in scientific computing support at the University of Chicago. This is a hybrid position requiring 3 days onsite.

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.
Proficiency in one or more compiled programming languages (e.g., C++, C, Julia, or Fortran).
Proficiency in Python or another scientific scripting language.
Experience working in Linux/UNIX environments and with high-performance computing (HPC) systems.
Experience with HPC job schedulers (e.g., Slurm).
Experience installing, optimizing, profiling, and supporting scientific software and workloads on HPC systems.
Excellent analytical, problem-solving, and communication skills, with the ability to work effectively with faculty and multidisciplinary teams.
Familiarity with scientific computing libraries such as NumPy, SciPy, pandas, xarray, and scikit-learn.
Experience with containers and development tools such as Docker, Apptainer/Singularity, and Git.
Experience with AI/ML frameworks such as PyTorch or TensorFlow.
Experience with LLM APIs and tooling (e.g., Anthropic, OpenAI, Hugging Face), AI coding assistants.
Experience serving or benchmarking models on GPU clusters.
Ability to translate researchers’ scientific goals into computational requirements.
Ability to work independently and as part of a collaborative team.
Demonstrated ability to evaluate and apply emerging technologies in research computing.

Nice To Haves

PhD in a relevant field.

Responsibilities

Partner with faculty and research groups as a domain expert to develop and implement computational solutions that advance their research.
Advise on the effective use of RCC resources, HPC systems, AI technologies, and cloud platforms.
Support researchers’ use of AI and machine learning tools, including AI coding assistants and agents (e.g., Claude Code), model APIs (e.g., Anthropic, OpenAI), and open-weight models hosted on RCC systems.
Develop, maintain, optimize, and support scientific software, computational workflows, and research computing environments on RCC systems.
Troubleshoot, profile, optimize, and port scientific applications to maximize performance across CPU, GPU, memory, storage, and I/O.
Contribute technical expertise to faculty projects through the RCC Consultant Partnership Program and other collaborative initiatives.
Contribute computational expertise to grant proposals, including scoping the AI, HPC, cloud, and storage resources committed to the project.
Develop and maintain technical documentation and knowledge base resources.
Contribute to the continuous improvement of RCC systems, services, operational practices, and user support processes.
Solves user problems promptly and professionally.
Proactively recommend appropriate RCC technologies and services.
Stay current with advances in AI, HPC, GPU computing, and cloud technologies, and evaluate emerging tools for adoption at the RCC.
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.