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We are building an AI-powered feedback tool to improve the quality of virtual 1:1 math tutoring. To support this initiative, we’re seeking an Tutor Quality Specialist who will evaluate tutoring session quality, define best-practice pedagogical frameworks, and collaborate with engineers on AI prompting. You’ll help lay the foundation for scalable, effective feedback loops that improve tutoring at scale. Core Responsibilities Session Evaluation & Scoring - Review a sample set of recorded tutoring sessions (~50–100 hours) and generate baseline quality scores based on research-backed framework -Surface tutor behaviors that correlate with strong or weak outcomes Framework & Prompt Development -Collaborate with engineers to test different prompting for extracting feedback from transcripts using LLMs -Define key attributes of effective tutoring for incorporation into prompt logic and scoring rubrics Feedback Design -Draft language and structure for clear, actionable tutor feedback - balancing directness with encouragement -Help shape the feedback tool’s tone, instructional framing, and alignment to pedagogical goals Iteration & Insights -Participate in regular design and engineering standups -Deliver concise weekly updates, surfacing insights that inform product decisions Ideal Candidate -Deep understanding of high-impact tutoring, instructional best practices, or teacher coaching -Experience in instructional design, teacher training, or education research -Comfort working cross-functionally with engineers and product folks on experimental tools -Ability to translate observation into structured frameworks (Bonus) Familiarity with LLMs, prompt engineering, or AI/edtech tools Deliverables -Annotated tutor quality rubric with baseline scores -Set of best-practice tutoring behaviors (with examples) -Prompt suggestions for eliciting high-quality AI-generated feedback -Feedback templates (or examples) for tutors -Final memo with insights and improvement recommendations