Staff AI Context Engineer Job at EdTech Jobs

Job Description

MagicSchool is seeking a Staff AI Context Engineer to architect and enhance the information infrastructure that powers AI agents for educational content retrieval and organization. This role focuses on designing knowledge graphs, implementing advanced retrieval systems, and mentoring engineers. As a Staff AI Context Engineer specializing in RAG, Knowledge Graphs, and Memory Systems, you'll architect the information infrastructure that powers MagicSchool's AI agents. You'll design and build the knowledge organization, retrieval, and memory systems that determine what educational content our agents can access, how they navigate complex curriculum relationships, and how they maintain coherent understanding across extended teaching workflows serving millions of educators.

Job Responsibility

Knowledge Graph & Semantic Architecture: Architect and implement graph-based knowledge systems (Neo4j, Neptune, etc) that represent educational content relationships, standards alignments, prerequisite chains, curriculum coherence, learning progressions, and pedagogical connections.
Graph Schema & Ontology Development: Design and evolve ontologies and schemas for educational content, defining entity types (standards, concepts, skills, assessments), relationship semantics, and property models.
GraphRAG Implementation: Build GraphRAG systems that combine knowledge graph traversal with vector similarity, enabling agents to retrieve contextually connected educational materials.
Retrieval Pipeline Architecture: Architect and implement sophisticated retrieval-augmented generation pipelines including hybrid search (dense + sparse), multi-stage retrieval, reranking strategies, and query understanding.
Embedding & Vectorization Strategy: Design and operationalize embedding pipelines for educational content, selecting and fine-tuning embedding models, implementing chunking strategies, and managing vector stores at scale.
Retrieval Evaluation & Optimization: Design evaluation pipelines that measure retrieval precision, recall, MRR, and NDCG across educational content types. Continuously optimize retrieval quality.
Document Ingestion & Processing: Build robust ingestion systems that process structured and unstructured educational content, extracting entities, relationships, and metadata for knowledge base population.
Semantic Parsing & Extraction: Implement NLP pipelines for educational content that extract key concepts, prerequisite relationships, learning objectives, and pedagogical metadata.
Memory & Context Management: Invent and operationalize memory compaction mechanisms, session state management, and cross-conversation memory patterns that allow agents to maintain coherence across extended teaching workflows.
Context Evaluation & Monitoring: Design evaluation frameworks that measure retrieval precision, token relevance, attention allocation, and reasoning coherence as context evolves across sessions.
Cross-Functional Collaboration: Partner with Product, Research, and Educators to understand content relationships, retrieval requirements, and context needs across different teaching scenarios.
Model & Platform Integration: Collaborate with ML researchers / evaluations team and context engineers to co-design architectures that integrate knowledge graphs, vector stores, and retrieval systems with agent runtimes and LLM inference pipelines.
Technical Mentorship: Guide engineers on knowledge graph design, RAG architecture patterns, embedding strategies, and retrieval optimization.

Requirements

Deep Knowledge Systems Experience: 5+ years building large-scale information systems with at least 2+ years in staff/senior roles. Extensive hands-on experience with RAG systems, knowledge graphs, or semantic search platforms in production environments.
Graph Database Expertise: Deep experience with graph databases (Neo4j, Neptune, or similar), including schema design, query optimization (Cypher, Gremlin), and building graph-based applications.
RAG & Retrieval Mastery: Demonstrated expertise building production RAG systems including embedding selection, chunking strategies, hybrid search, reranking, and retrieval evaluation. Familiarity with vector databases (pgvector, Pinecone, Weaviate, Qdrant).
Embedding & NLP Background: Strong understanding of embedding models (sentence transformers, domain-specific embeddings), fine-tuning approaches, and semantic similarity. Experience with document processing, entity extraction, and text chunking for optimal retrieval.
Technical Stack: Strong coding skills in Python and/or TypeScript/Node.js. Experience with our stack (TypeScript, Node.js, PostgreSQL, NextJS, Supabase) plus graph databases and vector stores. Familiarity with LLM APIs and context management patterns.
Information Architecture: Deep understanding of information retrieval theory, semantic search, knowledge representation, and strategies for organizing complex domain knowledge for both human and AI consumption.
Leadership & Impact: Track record of architecting complex knowledge systems, making high-leverage technical decisions about information architecture, and mentoring engineers on sophisticated retrieval and graph concepts.

Nice to have

Educational Context Awareness: Understanding of or interest in how educational content is structured (standards, curricula, learning progressions), curriculum relationships, and how knowledge organization differs across teaching scenarios.
Experience with GraphRAG, knowledge graph embeddings (node2vec, TransE), or graph neural networks for link prediction and entity resolution
Familiarity with educational knowledge graphs, standards alignment systems (CASE framework), or EdTech content taxonomies
Background in semantic web technologies (RDF, OWL, SPARQL), ontology engineering, or knowledge graph construction from unstructured text
Experience with model context protocol (MCP) for tool-based retrieval, or building context-aware agent frameworks
Knowledge of curriculum standards, learning science, or educational metadata schemas (LOM, schema.org/LearningResource)
Experience with fine-tuning embedding models for domain-specific retrieval or building learned sparse retrievers

What we offer

Flexibility of working from home.
Unlimited time off.
Choice of employer-paid health insurance plans. Dental and vision are also offered at very low premiums.
Generous stock options, vested over 4 years.
401k match.
Monthly wellness stipend.

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