As machine learning (ML) continues to revolutionize scientific fields, the necessity for scientific applications to integrate seamlessly with ML frameworks has become paramount. This integration often requires consistent derivatives and adapts to the evolving programming paradigms driven by ML accelerators, which are moving beyond traditional CPU threading. This shift is not just about hardware; the shift involves a complex interplay among hardware architecture, numerical methods, and programming techniques. Navigating these changes demands expertise across several domains, each decision influencing others, often shifting focus from scientific exploration to code development. During this session, we will address the programming challenges arising from the widening gap among domain specialists, mathematicians, and computer scientists in computational science. We aim to explore potential workflows and programming designs that enhance scientific productivity and discovery and reduce the technical complexities for domain specialists and mathematicians. Join us to discuss strategies that streamline the integration of advanced computational methods into scientific research, fostering more effective and efficient scientific inquiry.