• About the RoleWe'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 ProcessScreening ＞ Take-home Assignment ＞ Technical Interview ＞ Cultural Interview
