Full Stack Engineer (Los Altos)

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.

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
• 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