Develop deep expertise in prompt engineering and model tuning. Design and implement new relevance metrics to measure and improve local search quality. Develop and optimize LLM/SLM labeling pipelines for high-throughput, consistent quality judgments. Engineer and fine-tune prompts for LLMs to enhance query understanding and classification accuracy. Apply modern LLM techniques such as retrieval-augmented generation for improved grounding and relevance. Build scalable workflows and dashboards for measurement, evaluation cycles, and quality checks. Analyze failure modes and improve prompt rubrics to reduce defect rates and enhance labeling consistency. Collaborate with cross-functional teams to integrate metrics and labeling systems into production environments. Bachelor's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND hands on experience (e.g., statistics, predictive analytics, research) OR Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND hands on experience (e.g., statistics, predictive analytics, research) OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field OR equivalent experience. These requirements include but are not limited to the following specialized security screenings: