Full Stack Engineer (Los Altos)

Cheiron just raised an $8 million seed round led by Menlo Ventures to build the operating system for drug programs.

■ Company Overview

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.

■ 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