■ Company Overview Cheiron is a Stanford-founded AI company building an AI-native operating system for the pharma, biotech, and broader life sciences industry. Since launching in 2024, we've rapidly grown our global user base. In Korea, we reached 22% of industry users within six months and are now used across 200+ pharma and biotech companies as we continue to scale quickly. ■ About the Role We're looking for a Full Stack Engineer, Applied AI to help build Cheiron's core product and infrastructure. This role spans the full stack — backend systems, AI workflows, RAG and search pipelines, APIs, and the customer-facing product itself. You won't own just one slice; you'll work deeply across the product and systems and solve whatever problems need solving. We build products that run in real customer environments — not research demos or prototypes. We're looking for a hands-on builder who can quickly structure ambiguous problems and turn them into highly polished products. You don't need to be an AI researcher or a life sciences domain expert, but we care deeply about strong engineering fundamentals and real experience designing, building, and operating LLM systems. ■ What You'll Do - Design and build backend services on Python, FastAPI, and Postgres - Build applied AI workflows using the OpenAI API, LangGraph, vector DBs, and search systems - Develop ingestion, indexing, and search pipelines for life sciences data, including academic papers, clinical, regulatory, safety, and patent sources - Build customer-facing features end to end, from the data model to the API to the React/TypeScript frontend - Build systems that ground AI-generated outputs in source data with traceable, verifiable citations so they can be trusted in real pharma and biotech work - Work directly with founders, domain experts, and early customers, owning outcomes rather than just closing tickets ■ Requirements - 2+ years of experience shipping production software end to end - Strong backend engineering fundamentals and experience designing APIs, databases, and services - Hands-on production experience with AI-driven systems such as LLMs, agents, RAG, and vector DBs - Full-stack range, comfortable working through the frontend with React and TypeScript - The drive to set your own priorities and ship quickly, even when specs are incomplete - Active use of AI coding tools such as Claude Code and Cursor ■ Nice to Have - Hands-on experience designing and operating LangGraph, agent frameworks, or RAG systems in production - Experience with vector DBs, semantic search, and knowledge graphs - Experience building enterprise SaaS, or pharma, biotech, or healthcare products - Familiarity with life sciences domain data such as academic literature, clinical trials, regulatory documents, and patents - Experience at a seed or early-stage startup ■ Benefits & Perks - 401(K) retirement plan - Health insurances (Medical/Dental/Vision) - Meal allowance (Lunch, Dinner) - Transportation support for early starts and late nights - In-office snack bar and additional commuting and work travel support ■ Interview Process Screening ＞ Take-home Assignment ＞ Technical Interview ＞ Cultural Interview