The role involves creating large-scale data sets and developing training recipes for both human and machine labeling. The individual will design methods for data mining and automated data collection to support model update flywheels. Responsibilities include evaluating the real-world performance of models and identifying regressions during updates. The position requires designing and implementing machine learning data infrastructure to support scaling needs. Furthermore, the role involves collaborating with infrastructure and domain teams to address bottlenecks and streamline validation processes.

Candidates must possess a degree in Computer Science, Engineering, or a related technical field. A minimum of four years of professional experience in software engineering and machine learning is required. Proficiency in C++ and Python is necessary for the role. Applicants should have experience designing distributed systems for large-scale data processing and a foundational understanding of machine learning principles.