Modeling Systems Project Manager

SAIC is looking for a specialized Project Manager to lead complex Modeling Systems Division scientific software engineering, infrastructure, and high-performance computing (HPC) projects.

This role requires a blend of technical acumen in scientific software ecosystems, modern AI/ML applications, and robust project governance to support cutting-edge Earth system modeling.

The Modeling Systems Project Manager coordinates the development, infrastructure, and deployment of complex scientific software across on-premises and cloud high-performance computing (HPC) environments.

This role bridges the gap between scientific research and technical execution, managing everything from Earth system model workflows to integrating modern AI/ML technologies.

• Scientific Software Engineering: Manage software development life cycles using Agile and hybrid frameworks, with a strong focus on programming languages essential to GFDL modeling, specifically Python, Fortran, and C/C++.
• DevOps & Scientific Ecosystems: Oversee automated testing pipelines, version control (Git), and containerization platforms for automated deployments, while managing scientific software ecosystems, build systems (Spack), and library wrapping.
• HPC & Cloud Infrastructure: Lead deployments across heterogeneous computing architectures, including GPU-accelerated clusters, on-prem environments, and cloud-native systems (AWS, Google Cloud, Azure).
• Data & Workflow Management: Facilitate end-to-end workflows for quasi-operational Earth system model runs and development pipelines, coordinating the movement of large datasets within distributed computing environments and implementing data catalog and object store approaches.
• AI/ML Integration: Champion modern AI applications in science, such as Retrieval-Augmented Generation (RAG) for technical support chatbots, AI emulators for seasonal forecasting, and coding assistants like GitHub Copilot and Gemini.
• Governance & Business Acumen: Direct core PM domains (scope, scheduling, risk mitigation, resource allocation, quality), manage IT finances (Capex vs. Opex, cloud costs, software licensing), and translate complex technical requirements for executive stakeholders and external contractors.