Backend Engineer, Applied Agents (Seoul)

Cheiron raised $8 million in seed funding led by Menlo Ventures — building the operating system for drug programs.

■ COMPANY OVERVIEW

In July 2026, Cheiron announced an $8 million seed investment round led by Menlo Ventures (total funding of $13 million). Industry leaders including Moderna co-founder and MIT 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 head John Giannandrea have joined with investment and strategic support.

Cheiron is building the first AI-native operating system that represents an entire drug program as a single connected system. Cheiron's platform helps biopharma teams represent the full state of a drug development program — the claims, evidence, assumptions, risks, decisions, and commitments that determine the success or failure of a therapeutic — and allows them to reason and stress-test upon it. Less than 6 months since launch, tens of thousands of biopharma professionals are using Cheiron; it has been adopted by major drug developers, and 7 out of the top 10 biopharma companies in Korea are already using it.

Cheiron is expanding into the pharmaceutical CMC (Chemistry, Manufacturing, and Controls) domain. CMC is the area that governs how medicines are made, tested, and kept consistent throughout their entire product lifecycle. This work involves hundreds of regulatory commitments, post-approval changes, and country-specific submissions, yet today's field still operates relying on documents, spreadsheets, and the memories of personnel. Cheiron adds an intelligence and reasoning layer specialized for CMC regulations on top of these manual workflows.

Cheiron was founded in 2024 by AI researchers from Stanford. The team includes leaders with decades of experience in the pharma and biotech sectors, and the headquarters is located in Los Altos, California, USA. We are currently building and deploying products while rapidly expanding into the global market.

Work communication is primarily conducted in English, and you will work with the US-based Leadership / Engineering Team. After 6 months of employment, US Visa Sponsorship can also be considered.

■ POSITION INTRODUCTION

We are looking for a Backend Engineer, Applied Agents, to build the agent layer of Cheiron and the backend system that supports it.

Cheiron's agents do not just stop at generating answers. They must perform multiple steps within an actual drug development workflow, reliably call tools, execute code in isolated environments, remember the context of multi-step tasks, and deliver final results to users in a verifiable format. You will directly design and launch the agent runtime, harness, memory, and tool execution layers that support all of this.

We do not build research demos or prototypes; we build products that operate in real user environments. We are looking for a practical Builder who can quickly structure ambiguous problems and implement them into high-quality products. You do not need to be an AI researcher or a Life Science domain expert, but we value strong engineering fundamentals and experience in designing, building, and operating LLM systems.

■ KEY RESPONSIBILITIES

• Design, implement, and operate the agent layer of Cheiron products: agent runtime/harness, tool calling infrastructure, sandboxed code execution environments, agent memory, and reliability/observability of multi-step tasks

• Design and implement backend services and APIs based on Python, FastAPI, and Postgres

• Design and operate vector DB, semantic search, and Retrieval-Augmented Generation (RAG) systems, and tune search quality and latency

• Build large-scale collection, refinement, and indexing pipelines for life science data, including academic papers, clinical, regulatory, safety, and patent data

• Build systems used reliably in actual bio/pharma workflows so that AI-generated results are linked to evidence data and sources can be traced and verified

• Collaborate directly with founders, domain experts, and early customers, taking ownership at the outcome level rather than simple ticket processing

■ QUALIFICATIONS

• 2+ years of experience in launching and operating production backend systems from end to end

• Strong backend engineering fundamentals: deep understanding of API design, data modeling, databases, and system reliability

• Experience handling AI-based systems such as LLM, RAG, and vector DB in actual production

• Practical understanding of how Agent systems work: experience directly building or at least deeply tracing how agent runtime, harness, tool calling, sandboxing, and memory actually function

• The drive to set priorities and push through to launch even in environments with insufficient specifications

• Someone who possesses both fast deployment speed and balanced engineering judgment

• Active use of AI coding tools such as Claude Code, Cursor, etc.

■ PREFERRED QUALIFICATIONS

• (Most important) Experience directly building an in-house agentic harness: if you have designed and implemented internal tools/harnesses to boost your team's agentic development productivity, this is the strongest signal.

• Experience directly designing and operating LangGraph, agent frameworks, or RAG systems in a production environment

• Experience in developing products for Enterprise SaaS, or the pharma, biotech, or healthcare sectors

• Understanding of life science domain data such as academic literature, clinical trials, regulatory documents, and patents

• Experience in deploying and operating production systems based on AWS, Terraform, and Kubernetes, as well as experience related to stability and observability

• Experience in seed to early-stage startups

■ BENEFITS AND WELFARE

• (Corporate Card) Support for lunch/dinner expenses, support for early morning/late night transportation expenses

• Other in-house snack bar, support for business transportation expenses, etc.

• $300 per person per month support for AI tool subscriptions

■ RECRUITMENT PROCESS

Document Review ＞ Take-home Assignment ＞ Technical Interview ＞ Technical Interview (2nd) ＞ Cultural Interview