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. 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.

Cheiron is expanding into pharmaceutical CMC (Chemistry, Manufacturing, and Controls) teams. CMC governs how a drug is made, tested, and kept consistent across its entire commercial life. The work involves hundreds of regulatory commitments, post-approval changes, and cross-market submissions. Today it runs on documents, spreadsheets, and institutional memory. Cheiron augments manual workflows with a CMC regulatory-specific intelligence and reasoning layer.

Founded in 2024 by Stanford-trained AI researchers. The team includes leaders with combined decades of experience across pharma and biotech. Headquartered in Los Altos, California. We are building and deploying the product now with rapid expansion into global markets.

• 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