[Hiring] Volunteer, AI Transformation @Clinton Health Access Initiative

Role Description

CHAI is searching for a hands-on volunteer to join the AI Transformation team as a builder. This is a build-focused role. You’ll work directly with Claude Code and Claude Cowork to build AI tools for CHAI’s internal teams. This role suits someone who learns by building and wants to ship things. The AI Transformation team is new, and you’ll be joining at an early stage.

Responsibilities

• Build and ship AI tools in Claude Code and Claude Cowork for CHAI’s internal teams.
• Take tools through the full cycle from scoping to deployment.
• Contribute to process overhauls in the teams the tools support.
• Partner with CHAI teams to identify, assess, and prioritize AI use cases.
• Help maintain a clear pipeline from idea to shipped tool.
• Inform build-vs-buy decisions: off-the-shelf, light customization, or custom build.
• Contribute to feasibility assessments, tool evaluations, and phase-in/phase-out decisions.
• Support IT collaboration on integration, maintenance, and scaling of shipped tools.
• Help maintain reliability and data governance standards on shipped tools.
• Document reusable components, prompt frameworks, and implementation patterns so each project starts from a higher base.
• Track usage and impact of shipped tools.
• Support pilots and partner technically with the Innovation team.
• Support training and enablement work led by the Ops AI role.
• Any other task identified by the supervisor.

Qualifications

• Current student or recent graduate.
• Programming experience in Python or a similar language.
• Hands-on experience with Claude Code, Claude Cowork, or other AI-native development environments.
• Working knowledge of LLM APIs and prompt engineering, with a practical sense of what these tools can and can’t do.
• Comfortable starting before every detail is clear.
• Strong written and oral communication in English.
• Detail-oriented and able to work independently.
• Genuine interest in applied AI and in CHAI’s mission.

Advantages

• Background in computer science, engineering, data science, or a related technical discipline.
• Familiarity with version control (e.g., GitHub), code review, and basic testing practices.
• Experience with RAG, embeddings, or other ways to ground LLM outputs in specific data.
• Experience shipping a small project from idea to working tool. Personal projects count.
• Interest in global health, international development, or mission-driven work.