• Collaborate closely with domain experts to translate requirements into software that is correct, usable, and extensible. • Own and improve internal modeling stacks, including: • Refactoring and modularization for clarity and reuse • Testing strategies that match scientific software realities (golden tests, invariants, property-based testing where useful) • Performance profiling and optimization where it matters • Documentation and developer experience improvements • Design and implement APIs and interfaces that turn working examples into maintainable components. • Build configuration management patterns that make runs reproducible and debuggable. • Implement and maintain orchestration pipelines for simulation ensembles and data validation. • Establish versioning and release practices for internal tooling, including metadata management. • Work primarily in Python and Julia. • Integrate with ML-adjacent components and artifacts (inputs, outputs, model wrappers), without being responsible for inventing new ML methods.