Cheiron은 제약 개발을 위한 AI 운영 체제를 구축할 Full Stack Engineer를 모집합니다. Python, React, LLM 및 RAG 시스템을 활용한 프로덕션 환경의 서비스 개발 경험이 필수입니다. 2년 이상의 실무 경력을 갖춘 분을 찾으며, 복잡한 문제를 해결하고 제품을 빠르게 출시할 수 있는 역량이 중요합니다. 캘리포니아 로스앨토스에서 근무하며, AI 기반의 혁신적인 바이오파마 플랫폼을 함께 만들어갈 인재를 기다립니다.
Cheiron just raised an $8 million seed round led by Menlo Ventures to build the operating system for drug programs.
In July 2026, Cheiron announced an $8 million seed round led by Menlo Ventures, bringing total funding to $13 million to date, with the backing and strategic support of industry veterans including Moderna co-founder and MIT Institute Professor Robert Langer, former Pfizer Chief Medical Officer Freda Lewis-Hall, Chai Discovery co-founder and CEO Josh Meier, former Starbucks CEO Laxman Narasimhan, and former Apple AI chief John Giannandrea.
Cheiron is building the first AI-native operating system designed to represent an entire drug program as a single connected system. The company's platform helps biopharma teams represent, reason over, and stress-test the full state of a drug development program — including the claims, evidence, assumptions, risks, decisions, and commitments that determine whether a therapy advances.
At the center of the platform is Cheiron's proprietary Life Sciences Knowledge Graph (LKG), connecting the world's biomedical, clinical, regulatory, patent, and commercial knowledge into a single model built for the inferences drug developers make every day. In less than six months since launch, Cheiron has been adopted by tens of thousands of biopharma professionals and deployed by major drug developers, and is already used by 7 of Korea's top 10 biopharma companies.
Founded in 2024 by Stanford-trained AI researchers and biopharma operators, Cheiron is headquartered in Los Altos, California.
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.
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
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
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
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
Screening ＞ Take-home Assignment ＞ Technical Interview ＞ Cultural Interview
