Cheiron에서 Backend Engineer를 채용합니다. LLM, RAG, Agent 시스템을 활용해 신약 개발 플랫폼의 에이전트 계층을 설계하고 구축하는 역할을 수행합니다. 2년 이상의 백엔드 개발 경험과 에이전트 프레임워크 운영 능력이 필수입니다. 실제 프로덕션 환경에서 AI 시스템을 구축해 본 경험이 중요하며, 빠르게 문제를 해결하고 제품을 출시할 수 있는 역량을 갖춘 분을 찾습니다.
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 Backend Engineer, Applied Agents to help build Cheiron's agent layer and the backend systems that power it.
Cheiron's agents don't just generate answers. They carry out multi-step tasks inside real drug development workflows, call tools reliably, execute code in sandboxed environments, retain context across multi-step work, and deliver verifiable results to users. You'll design and ship the agent runtime, harness, memory, and tool execution layers that make all of this possible.
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, build, and operate Cheiron's agent layer: agent runtime/harness, tool-calling infrastructure, sandboxed code execution, agent memory, and the reliability and observability of multi-step tasks
Design and build backend services and APIs on Python, FastAPI, and Postgres
Design and operate vector DB, semantic search, and RAG systems, and tune search quality and latency
Build large-scale ingestion, cleaning, and indexing pipelines for life sciences data, including academic papers, clinical, regulatory, safety, and patent sources
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 and operating production backend systems end to end
Strong backend engineering fundamentals: a deep understanding of API design, data modeling, databases, and system reliability
Hands-on production experience with AI-driven systems such as LLMs, RAG, and vector DBs
Practical understanding of how agent systems actually work: you've built — or at least deeply traced — agent runtimes, harnesses, tool calling, sandboxing, and memory
The drive to set your own priorities and ship quickly, even when specs are incomplete
A balance of fast shipping velocity and sound engineering judgment
Active use of AI coding tools such as Claude Code and Cursor
(Most important) Experience building an in-house agentic harness: if you've designed and built internal tools or harnesses to boost your team's agentic development productivity, that's the strongest signal for us
Hands-on experience designing and operating LangGraph, agent frameworks, or RAG systems in production
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 deploying and operating production systems on AWS, Terraform, and Kubernetes, with a focus on reliability and observability
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
