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Applied AI Engineer

Edison Scientific - San Francisco, CA

Engineer Verified May 21, 2026 Source: edisonscientific.com

This role is part of the Internal AI Job Board, a curated list of jobs for teams building or operating internal AI systems. Follow the original role page for the employer description, requirements, and application flow.

Compensation: Salary $220,000 - $350,000 • Offers equity WHY JOIN US? Competitive salary and equity Full healthcare cove

Responsibilities

  • Architect, implement, and maintain the AI agents that power Edison's platform, from prototype through production.
  • Work with our internal science and engineering teams to explore new agent architectures, prompting strategies, tool integrations, and evaluation frameworks.
  • Develop reusable infrastructure – agent skills, tool-use pipelines, benchmarks – that improves every agent on the platform.
  • Spend time with R&D partners (~30%) to understand their workflows, test what you've built in real environments, and bring insights back to the team.
  • Translate field insights and internal research into product direction – help the team prioritize what to build next.

Qualifications

  • 5+ 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.
  • Able to work across engineering, science, and product teams.
  • Comfortable building from scratch, driving clarity in ambiguous situations, and wearing multiple hats.
  • 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.
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