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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 owning the development and maintenance of production agents like Kosmos. Your primary focus is making these agents more capable, more reliable, and more useful across the life sciences.
Key responsibilities include:
- Architecting, implementing, and maintaining AI agents from prototype through production
- Exploring new agent architectures, prompting strategies, tool integrations, and evaluation frameworks with internal science and engineering teams
- Developing reusable infrastructure such as agent skills, tool-use pipelines, and benchmarks that improve every agent on the platform
- Spending approximately 30% of your time with R&D partners to understand their workflows, test solutions in real environments, and bring insights back to the team
- Translating field insights and internal research into product direction and helping prioritize what to build next
You will work across engineering, science, and product teams in a fast-moving, mission-driven culture.
REQUIREMENTS:
- 4+ years of professional software engineering experience with production systems that real users depend on
- Experience building LLM-powered tools or applications, including prompting, context engineering, agent architectures, and evaluation frameworks
- Strong engineering foundation in Computer Science, Software Engineering, Mathematics, Physics, Data Science, or related technical field
- Proficiency in Python and/or TypeScript with ability to pick 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
