Role Description We’re looking for a Senior Software Engineer to help evolve our Machine Learning capabilities, with a particular focus on closing feature gaps and behavioral differences relative to widely used ML frameworks (e.g., Spark ML, scikit-learn), while continuing to deliver new ML functionality. This role is ideal for someone who enjoys working across model behavior, system design, and customer expectations — ensuring that ML features behave predictably, perform well at scale, and align with how users expect industry-standard tools to work. Responsibilities - 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. Qualifications - 5+ years of experience building production software systems. - Strong proficiency in at least one backend or systems language (e.g., C++, Java, Scala). - Experience implementing or integrating machine learning models in production. - Familiarity with ML libraries or frameworks such as Spark ML, scikit-learn, XGBoost, or similar. - Strong instincts around correctness, edge cases, and behavioral consistency. - Ability to work across teams and codebases to turn ambiguous requirements into concrete solutions. Requirements - Experience comparing or validating behavior across multiple ML frameworks. - Experience with large-scale data systems or analytical databases. - Familiarity with distributed execution, performance tuning, or numerical stability. - Understanding of spherical geometry and its application to geospatial analytics. What success looks like - Customers see fewer surprises when using ML features compared to familiar frameworks. - ML behavior, defaults, and limitations are well-documented and intentional. - Feature gaps are identified early, not discovered under customer pressure. - You deliver across parity work and broader ML initiatives, balancing short-term needs with long-term quality.