The role involves developing importance sampling techniques to improve the efficiency of evaluation pipelines. The individual will identify signals within logs and simulations to discover rare and significant events. The position requires building systems that optimize multiple objectives while adhering to resource constraints. Collaboration with engineers, data scientists, and statisticians is necessary to deliver evaluation products and support data-driven decision-making. Additionally, the role entails maintaining code health and implementing best practices within a complex codebase.

Candidates must possess a Bachelor of Science degree in Computer Science, Robotics, Statistics, Physics, Math, or a related quantitative field. The role requires two to three years of experience in navigating large codebases, performing statistical analyses, and building data processing pipelines. Proficiency in probability, statistics, and coding standards for maintainability is mandatory. A PhD in a quantitative field is considered a preferred qualification.