How to apply for Vice President, The AI Access Initiative

About Evidence Action

Evidence Action scales cost-effective, evidence-based programs that reduce poverty in Africa and Asia. The AI Access Initiative (2AI) is incubated at Evidence Action and focuses on scaling AI-enabled programs for people in LMICs. It is led by former Evidence Action CEO Kanika Bahl and advised by Nobel Laureate Michael Kremer and Anthropic CEO Dario Amodei.

About the role

The Vice President will lead The AI Access Initiative, an organization incubated at Evidence Action. The role involves scaling AI-enabled 'big bets' in agriculture and health to benefit tens or hundreds of millions of people in low- and middle-income countries. The VP will operate at the intersection of global development actors, top AI labs, and leading researchers, and will create open-sourced playbooks and toolkits.

A typical day

A typical day might involve meeting with AI researchers to discuss a new model for crop disease detection, then coordinating with development partners to plan a pilot in Kenya. Later, you might review progress on the health program and draft a playbook for scaling AI-enabled community health worker tools. You would also spend time fundraising and engaging with advisors like Michael Kremer and Dario Amodei.

Who Evidence Action is looking for

• Experience leading large-scale programs or organizations in global development or technology, particularly in LMICs.
• Deep understanding of AI capabilities and how they can be applied to development challenges, with a track record of bridging research and implementation.
• Proven ability to work with diverse stakeholders including AI labs, researchers, and development organizations.
• Strong strategic and operational skills to scale programs and expand a portfolio, with experience in fundraising and partnership development.

Tips for this application

• Research Evidence Action's existing programs and how 2AI fits into their portfolio, then articulate how you would scale the AI in Agriculture and AI in Health initiatives.
• Highlight any experience you have working with AI labs, researchers, or development actors to deploy AI solutions in LMICs.
• Emphasize your ability to create and share open-source resources like playbooks and toolkits, as this is a key output of 2AI.
• Demonstrate familiarity with the advisors and leadership: Kanika Bahl, Michael Kremer, and Dario Amodei, and explain why their involvement matters to you.
• Show that you understand the urgency: AI capabilities advance quickly while development systems move slowly, so describe how you would accelerate impact.

What to cover in your cover letter

Explain why you want to bridge AI and global development at this inflection point. Describe a specific example of scaling an AI-enabled program in a low- or middle-income country. Outline how you would build partnerships with AI labs and researchers. Detail your approach to creating open-source playbooks and toolkits that others can use.

Research before applying

• Read Evidence Action's website, especially their approach to scaling evidence-based programs and their current initiatives.
• Learn about The AI Access Initiative's focus areas: AI in Agriculture and AI in Health, and any public materials about their strategy.
• Research the backgrounds of Kanika Bahl, Michael Kremer, and Dario Amodei, and understand their roles in 2AI.
• Look into existing AI for development projects in LMICs to understand the landscape and potential partners.

Likely interview topics

Based on the job description, expect questions about:

• How would you prioritize between the AI in Agriculture and AI in Health programs?
• Describe a time you scaled a technology program in a low-resource setting. What challenges did you face and how did you overcome them?
• How would you engage with AI labs like Anthropic and researchers like Michael Kremer to drive impact?
• What metrics would you use to measure the success of 2AI's programs?
• How would you ensure that AI benefits reach the poorest populations in LMICs, rather than exacerbating inequality?

Common mistakes to avoid

• Focusing only on AI technical details without connecting them to development outcomes and scaling.
• Ignoring the open-source and public good aspect of the role; candidates who emphasize proprietary solutions may not fit.
• Underestimating the need to work with multiple stakeholders; failing to show experience in partnership building across sectors.