3 months ago
Seoul, Korea, SouthMid Level
Responsibilities
- Design, implement, and operate Cheiron’s agent layer, including the agent runtime, harness, tool-calling infrastructure, sandboxed code execution, agent memory, and reliability and observability for multi-step tasks.
- Design and build backend services and APIs using Python, FastAPI, and Postgres.
- Design and operate vector database, semantic search, and RAG systems while tuning search quality and latency.
- Build large-scale collection, cleaning, and indexing pipelines for scientific papers, clinical, regulatory, safety, patent, and other life-sciences data.
- Build systems that connect AI-generated results to evidence data and support traceable, verifiable outputs for biotechnology and pharmaceutical workflows.
- Collaborate directly with founders, domain experts, and early customers while owning outcomes beyond individual tickets.
Requirements
- At least 2 years of experience shipping and operating production backend systems end to end.
- Strong backend fundamentals, including API design, data modeling, databases, and system reliability.
- Production experience with LLM, RAG, vector databases, or other AI-based systems.
- Practical understanding of agent systems, including agent runtimes, harnesses, tool calling, sandboxing, and memory, gained through building or deeply investigating these systems.
- Ability to set priorities and ship quickly in an environment with incomplete specifications.
- Ability to balance rapid deployment with sound engineering judgment.
- Active use of AI coding tools such as Claude Code and Cursor.
- Preferred: experience building an internal agentic harness to improve developers’ agentic productivity.
- Preferred: production experience designing and operating LangGraph, agent frameworks, or RAG systems.
- Preferred: enterprise SaaS or pharmaceutical, biotechnology, or healthcare product development experience.
- Preferred: understanding of life-sciences data such as academic literature, clinical trials, regulatory documents, or patents.
- Preferred: production deployment and operations experience with AWS, Terraform, and Kubernetes, including reliability and observability.
- Preferred: experience at a seed-stage or early-stage startup.
Benefits
- Lunch and dinner meal support through a company card.
- Transportation support for early-morning work and late-night work.
- In-office snack bar and business transportation support.
- Work communication is primarily in English with US-based leadership and engineering teams.
- US visa sponsorship may be reviewed after six months of employment.
- Hiring process: screening, take-home assignment, technical interview, and cultural interview.
Tech Stack
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