Staff AI Context Engineer

November 5

🏄 California – Remote

Although this job is listed in California, this company may be open to hiring remote in other states.

💵 $205k - $240k / year

⏰ Full Time

🔴 Lead

🤖 Artificial Intelligence

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MagicSchool AI

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Education • Artificial Intelligence

MagicSchool AI is a leading artificial intelligence platform tailored for the education sector. It offers over 70 AI tools that assist educators in lesson planning, writing assessments, and communicating effectively. The platform is embraced by over 4 million educators worldwide and integrates with learning management systems like Google Classroom and Microsoft Word. MagicSchool AI emphasizes responsible AI use, supporting both educators and students in building AI literacy while ensuring privacy and compliance with regulations. The platform offers a wide range of resources to enhance learning experiences, save educators time, and promote AI literacy, making it a valuable tool for modern education needs.

11 - 50 employees

Founded 2023

📚 Education

🤖 Artificial Intelligence

💰 $2.4M Pre Seed Round on 2023-08

📋 Description

• Architect the information infrastructure that powers MagicSchool's AI agents. • Design and build the knowledge organization, retrieval, and memory systems for educational content. • Define how educational knowledge is structured, indexed, embedded, and retrieved for AI consumption. • Mentor engineers on advanced retrieval and graph systems. • Ensure our agents can reason over rich educational content with precision and reliability.

🎯 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) and their performance characteristics. • Embedding & NLP Background: Strong understanding of embedding models (sentence transformers, domain-specific embeddings), fine-tuning approaches, and semantic similarity. • 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. • 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.

🏖️ Benefits

• Unlimited time off to empower our employees to manage their work-life balance. • Choice of employer-paid health insurance plans so that you can take care of yourself and your family. Dental and vision are also offered at very low premiums. • Every employee is offered generous stock options, vested over 4 years. • Plus a 401k match & monthly wellness stipend