Role Description

• Define how MagicSchool’s AI agents think, reason, retrieve information, and maintain coherence across complex educational workflows.
• Translate company vision and goals into a product strategy applying deep systems, retrieval, and knowledge-graph concepts to power intelligent agent behavior.

Responsibilities

• Define and own the product strategy for context, memory, and retrieval systems determining AI agents' information access and response grounding.
• Partner with Context Engineering and Knowledge Graph Engineering to convert advanced technical capabilities into product requirements and shipped features.
• Drive end-to-end development of context pipelines and AI reasoning systems balancing token efficiency, retrieval precision, and classroom reliability.
• Design measurement frameworks for context quality, retrieval performance, knowledge grounding, and long-horizon coherence to prioritize improvements and ensure correctness.
• Represent educators and classroom workflows with empathy and collaboration to define necessary information and tools for AI assistance.

Experience & Qualifications

• 5+ years of product management experience including platform, AI systems, agentic workflows, retrieval systems, or technically complex backend products.
• Strong understanding of AI context management with experience in LLMs, agent architectures, prompt strategies, context windows, embeddings, RAG, memory systems, or knowledge representations.
• Technical fluency with data systems including knowledge graphs, database optimization, entity/relation modeling, retrieval pipelines, and relevance tuning.
• Ability to partner deeply with engineering, translating distributed systems constraints and complex architectures into product strategy and requirements.
• Use qualitative and quantitative data to drive product decisions.
• Exceptional execution and prioritization skills in highly technical environments with complex interdependencies.
• Clear, persuasive communication skills to align teams on technical concepts.

Required Experience

• Experience shipping platform-level or AI/ML-driven systems involving context, retrieval, or structured information.
• Hands-on work with LLM-based products, particularly RAG architectures, memory systems, vector search, or dynamic retrieval.
• Experience with data engineering concepts including knowledge graphs, entity linking, schema design, database/query optimization, and semantic search.
• Strong background with complex tech stacks such as Python, TypeScript/Node.js, relational and vector databases.
• Proven track record in improving system quality, reliability, or correctness.

Nice to Have

• Direct experience with knowledge graph systems, graph query languages, or semantic data modeling.
• Experience in agentic system performance, context quality, retrieval precision, token efficiency, or long-horizon coherence.
• Background in hybrid retrieval systems combining structured graphs with vector or embedding-based search.
• Familiarity with educational data structures, FERPA/COPPA requirements, or EdTech ecosystems.
• Experience in domains requiring complex relationship modeling or high correctness such as finance or healthcare.

Why Join Us?

• Work on cutting-edge AI technology impacting educators and students.
• Join a mission-driven team making education more efficient and equitable.
• Enjoy flexible work-from-home with a culture based on trust, communication, and collaboration.
• Full-time employees receive unlimited time off, employer-paid health insurance, dental and vision options, generous stock options vested over 4 years, 401k match, and wellness stipend.