Full Stack Engineer, Applied AI

Company Overview

Cheiron is building an AI-native operating system for drug development: a connected view of the evidence, assumptions, risks, decisions and commitments behind a drug program. The goal is to help biopharma teams understand what is known, test their assumptions and see how a change affects the rest of a program.

Our current focus is CMC (Chemistry, Manufacturing, and Controls): how a drug is made, tested and kept consistent throughout its commercial life. A manufacturing change can create a chain of evidence, review and submission work across markets. Today, teams piece this work together across documents, spreadsheets and institutional memory. We're building agents that connect the relevant evidence, reason through requirements and help teams prepare and review the resulting submissions.

Founded in 2024 by Stanford-trained AI researchers, Cheiron is a Seed-stage company headquartered in Los Altos, California. Our team of 19 people, including 8 engineers, combines AI and software engineering with experience from pharma and biotech. The platform has been adopted by tens of thousands of biopharma professionals. With $16M total raised, including an $11M seed round led by Menlo Ventures, we're growing our engineering team in Los Altos and Seoul.

■ 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