Staff Software Engineer, MagicSchool AI

• Deep Systems Experience – 7+ years building large-scale distributed systems with at least 2+ years in staff/senior roles. Experience with agent orchestration, workflow engines, or autonomous system infrastructure
• Agent & AI Systems – Hands-on experience building agent applications with tool calling, context engineering, multi-step reasoning, or autonomous workflow orchestration. Familiarity with LLM integration patterns
• Technical Stack – Strong coding skills in Python, TypeScript/Node.js, or Go. Experience with our stack (TypeScript, Node.js, PostgreSQL, NextJS, Supabase) or similar technologies
• Infrastructure & Security – Working knowledge of containers, virtualization, sandboxing, and security patterns for multi-tenant systems. Experience with metrics, observability, and incident response
• Educational Context Awareness – Understanding of or interest in educational workflows, privacy requirements (FERPA/COPPA), and safety considerations for AI in classroom environments
• Leadership & Impact – Track record of architecting complex systems, mentoring engineers, and driving technical decisions that balance innovation with reliability

Desirable

• As a Staff Software Engineer for Agents Infrastructure, you'll architect and build the core systems that power MagicSchool's autonomous AI capabilities
• You'll design the runtime, orchestration, and evaluation infrastructure that enables AI agents to plan, execute complex educational workflows, integrate with teaching tools, and operate safely in classroom environments
• This is a high-impact IC role where you'll influence the technical foundation of how AI agents operate across MagicSchool's platform, mentor engineers on agent systems, and ensure our agentic capabilities scale reliably for millions of educators
• Agent Runtime Architecture – Design and implement highly reliable agent orchestration systems, shared state management, memory systems, and multi-step workflow execution optimized for educational use cases
• Tool Integration & Safety – Build secure, sandboxed execution environments for agent actions with robust isolation, monitoring, and safety controls appropriate for educational contexts
• Evaluation & Reliability – Develop comprehensive evaluation frameworks for agent performance, safety assessments, A/B testing, and regression detection to ensure consistent quality in educational workflows
• Platform Integration – Create unified interfaces for multiple LLM providers, integrate with educational tool ecosystems, and build connector frameworks for curriculum data and learning management systems
• Cross-Functional Leadership – Partner with Product, Research, and Education teams to translate complex educational workflows into reliable agent capabilities from prototype to production
• Technical Mentorship – Guide engineers on agent architecture patterns, evaluation methodologies, and best practices for building reliable autonomous systems
• Performance Optimization – Optimize agent execution for cost, latency, and quality while maintaining the reliability standards required for educational environments
• Technical Leadership – Lead the architecture and implementation of MagicSchool's core agent infrastructure, making high-leverage decisions that enable reliable autonomous AI in education
• End-to-End Ownership – Own agent systems from research prototype to production deployment, ensuring they meet the reliability and safety standards required for educational use
• Cross-Functional Partnership – Collaborate closely with Product, Research, and Education teams to understand complex educational workflows and translate them into scalable technical solutions
• Mentorship & Knowledge Sharing – Coach engineers on agent architecture patterns, evaluation methodologies, and best practices for building reliable autonomous systems
• Innovation & Reliability – Balance cutting-edge AI capabilities with the rock-solid reliability and safety requirements of educational technology