Staff Context Engineer, AI Systems

7+ years building distributed systems, with at least 2+ years in staff/senior roles. Hands-on experience building LLM applications, agentic systems, or context-heavy AI workflows. Clear understanding of transformer architectures and attention mechanisms. Demonstrated experience managing context windows, building dynamic retrieval mechanisms, or designing context compaction strategies. Strong coding skills in Python and TypeScript/Node.js. Familiarity with LLM APIs (OpenAI, Anthropic, etc.) and their context management patterns. Understanding of information retrieval, structured data representation, and knowledge organization for AI. Understanding of or interest in educational content structure, privacy requirements, and teaching scenario context needs. Track record of architecting information systems and mentoring engineers.

Architect and implement adaptive context curation pipelines for LLM inference. Invent and operationalize memory compaction mechanisms and state management patterns for agents. Design evaluation pipelines for retrieval precision, token relevance, and reasoning coherence. Build dynamic, runtime data fetching systems for agents. Engineer token-efficient tool APIs and retrieval layers. Partner with Product, Research, and Education teams on context configurations. Collaborate with ML researchers and platform engineers on memory module integration. Guide engineers on context engineering patterns and holistic context management.