• Design and implement machine learning features used in production customer workflows. • Help identify and close feature and behavior gaps between our ML capabilities and common frameworks (e.g., Spark ML, scikit-learn). • Proactively evaluate semantic differences, defaults, and edge cases that could surprise customers. • Partner with product, architects, and customer-facing teams to anticipate upcoming customer needs and gaps. • Investigate and resolve issues where ML behavior diverges from user expectations (e.g., model output, metrics, configuration semantics). • Contribute to other ML initiatives including new models, metrics, performance improvements, and infrastructure work. • Analyze and improve the performance of existing ML code, balancing correctness and stability with customer facing latency. • Write clear design docs, tests, and documentation to make behavior explicit and prevent regressions.