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Edison Scientific builds and deploys AI scientist agents to accelerate science and the development of new medicines. The company is run by scientists and engineers from leading institutions across biology, physics, chemistry, and AI.
You will join the team responsible for building Edison's AI agents, specifically working on Kosmos, the company's AI scientist agent. In this role, you'll own the development and maintenance of production agents, prototype new capabilities with internal science and engineering teams, and shape how the platform evolves.
Key responsibilities include:
- Architecting, implementing, and maintaining the AI agents that power Edison's platform, from prototype through production
- Working with internal science and engineering teams to explore new agent architectures, prompting strategies, tool integrations, and evaluation frameworks
- Developing reusable infrastructure such as agent skills, tool-use pipelines, and benchmarks that improve every agent on the platform
- Spending time with R&D partners (approximately 30% of your time) to understand their workflows, test what you've built in real environments, and bring insights back to the team
- Translating field insights and internal research into product direction to help the team prioritize what to build next
You'll spend some time with customers to understand real workflows and validate what you're building, but your primary focus is on the agents themselves—making them more capable, more reliable, and more useful across the life sciences.
REQUIREMENTS:
- 4+ years of professional software engineering experience, with production experience shipping systems that real users depend on
- Experience building LLM-powered tools or applications: prompting, context engineering, agent architectures, evaluation frameworks
- Strong engineering foundation in Computer Science, Software Engineering, Mathematics, Physics, Data Science, or a related technical field
- Proficiency in Python and/or TypeScript, with comfort picking up new tools and frameworks quickly
- Ability to work across engineering, science, and product teams
- Comfortable building from scratch, driving clarity in ambiguous situations, and wearing multiple hats
PREFERRED QUALIFICATIONS:
- Experience in life sciences, biomedical research, scientific computing, or technical R&D workflows
- Experience building agent systems, tool-use pipelines, or evaluation/benchmarking frameworks for AI applications
- Contributions to open-source scientific or AI tooling
