Job Purpose:
The Manager of Scientific Computing Support provides both hands-on technical expertise and leadership to deliver scientific computing support services to the research community. This role entails active involvement in the design, optimization, and troubleshooting of advanced computational services, including HPC, AI/ML workflows, LLM workloads, and GPU-accelerated research support, as well as the management of a team that provides advanced user support, guidance, and training. By ensuring system reliability, scalability, and alignment with researchers’ computational needs, the manager enables scientists to effectively leverage HPC resources for complex workflows and cutting-edge research projects.
This role requires deep knowledge of scientific computing as well as an applied understanding of AI systems, enabling researchers to deploy, benchmark, optimize, and run modern AI/ML models at scale. The manager oversees a team responsible for supporting traditional HPC workflows and emerging AI workloads, ensuring KU remains at the forefront of computational research capability.
The manager also contributes to the development of AI-driven tools to enhance internal operations, improve research productivity, and support departmental automation initiatives.
Key Roles & Responsibilities:
Strategic Responsibilities
Contribute to long-term strategies for HPC, AI, ML, and data-intensive research support within the University. Identify emerging technologies and advise leadership on future investments.
Operational Responsibilities
Coordinate and prioritize team tasks to meet project deadlines and ensure high-quality support services. Provide advanced technical support for scientific computing applications, HPC workflows, AI/ML workflows, and infrastructure. Troubleshoot and resolve complex computational issues, collaborating with researchers to understand and address their unique requirements. Oversee the design, implementation, and maintenance of HPC and AI/ML environments. Oversee the design, implementation, and maintenance of HPC and AI/ML environments. Optimize system performance, scalability, and reliability to meet the computational demands of scientific research projects, including both traditional HPC workloads and emerging AI workloads. Engage with research teams to understand their computational requirements and provide guidance on best practices. Manage and allocate computational resources efficiently to meet the diverse needs of various research projects. Develop and maintain comprehensive documentation for scientific computing systems, processes, and best practices. Develop documentation, and best practice guides for HPC, GPU, and AI/ML infrastructure usage. Deliver training workshops for researchers and team members to enhance their understanding of scientific computing resources and methodologies. Plan and organize outreach events to promote scientific and AI computing capabilities within the research community and beyond. Stay informed about emerging trends and technologies in scientific computing and contribute to the development of strategic plans. Collaborate with external partners, institutions, and organizations to participate in or host workshops, conferences, and seminars on scientific computing.
Supervisory Responsibilities
Coordinate, prioritize, and oversee daily team activities to ensure timely delivery of high-quality scientific computing support services. Mentor, coach, and develop team members, fostering a collaborative and technically strong work environment. Establish and track performance metrics, service levels, and operational workflows to continually improve team effectiveness.