You will work on simulation automation that matters to real engineering teams solving real problems, and you will have the space to build tools the right way.
Design and build FEA model conversion tools that translate input files across Abaqus, Nastran, LS-Dyna, and Ansys APDL, handling the messy edge cases that break template-driven approaches.
Develop Python automation using pyAnsys modules like PyMAPDL, PyDPF, and PyFluent to streamline simulation setup, execution, and post-processing workflows.
Use AI coding assistants intensively as a development accelerator, then rigorously test, review, and validate every line of AI-generated code to ensure it meets production standards.
Act as a technology advocate by working directly with customers and internal engineering teams to drive adoption of new automation tools, deliver hands-on training, and gather feedback that shapes the roadmap.
Collaborate with multidisciplinary R&D teams to understand their simulation pain points and translate them into reliable, maintainable software solutions.
Contribute to documentation, best practices, and knowledge-sharing that help broader teams adopt pyAnsys workflows and AI-assisted development methods.
Own the full development lifecycle from scoping and prototyping through testing, release, and ongoing improvement based on real-world usage.
The impact you will have includes cutting manual model preparation time and error rates, raising team productivity, demonstrating AI-augmented engineering, driving measurable adoption of modern simulation workflows, contributing directly to Synopsys simulation automation strategy, building organisational capability, and shaping the future direction of Ansys automation tooling.
XML job scraping automation by YubHub
