• Define and own what “good” means for search-augmented and agentic AI systems by designing evaluation frameworks that measure real-world quality, reliability, and user-relevant behavior beyond standard benchmarks. • Invent and validate novel evaluation methodologies for non-deterministic systems (LLMs, agents, RAG), including behavioral evals, long-tail and adversarial test sets, and task-specific metrics. • Develop rigorous statistical frameworks for model comparison, regression detection, and uncertainty estimation, ensuring evaluation results are defensible and decision-ready. • Build and maintain scalable evaluation systems—datasets, gold standards, eval harnesses, scoring pipelines, and analysis tooling—that can be reused across products and customers. • Lead customer-facing evaluation research, working directly with enterprise customers to translate domain-specific quality requirements into credible, actionable evals that support product decisions and sales outcomes. • Drive competitive evaluations and internal quality reviews, surfacing meaningful performance differences, trade-offs, and failure modes to inform product strategy and prioritization. • Partner with engineering and product teams to integrate evals into development loops, release gating, and ongoing quality monitoring. • Mentor and set standards for evaluation practice, reviewing eval designs, guiding other scientists, and shaping the long-term evals roadmap as systems become more agentic and complex. • End-to-End Project Leadership: Lead the development of new AI-driven projects, encompassing ideation, prototyping, research, infrastructure design, scalability, monitoring, and evaluation. • Rapid Iteration: Adapt quickly to user feedback and evolving requirements, ensuring continuous improvement in a fast-paced environment.