Director, Monitoring, Learning, and Evaluation, AI Access Initiative

About The AI Access Initiative (2AI)

We're at an inflection point in artificial intelligence, presenting both tremendous potential opportunity and risk for people in developing countries. AI capabilities are improving on a timescale measured in months, while development systems often move on timescales measured in decades. Much of the technology needed for positive impact exists today, but few organizations exist to bridge research, deployment, and scaled implementation: we must act now to ensure AI's benefits reach people in poverty in LMICs.

Incubated at Evidence Action, we're scaling The AI Access Initiative, an organization focused on scaling AI-enabled 'big bets' to benefit tens or hundreds of millions of people in poverty in low- and middle-income countries (LMICs). We operate at the intersection of global development actors, top AI labs, and leading researchers to drive meaningful access to AI's benefits for the 3.5 billion people living in poverty globally. We create "public good," open-sourced playbooks, toolkits, and insights that define how to design and launch tractable and impactful AI-enabled programs. Given the scale of opportunity, we expect our portfolio to expand substantially, but to start, we're scaling two programs focused on AI in Agriculture and AI in Health.

We are led by former Evidence Action CEO Kanika Bahl, a founding member of Anthropic's Long-Term Benefit Trust, and advised by Nobel Laureate Michael Kremer; Dario Amodei, CEO of Anthropic; and Kent Walker, President, Global Affairs for Alphabet and Google. This work builds on Evidence Action's track record reaching 530M+ people with cost-effective, evidence-based programs across 9 countries in Africa and Asia, with a focus on last-mile delivery.

The Role

We are seeking a Director of Monitoring, Learning, and Evaluation (MLE) to build and lead 2AI's MLE function from the ground up. You will set the evidence standard for our live Agriculture and Health programs and for new interventions developed by our Incubator, and you will be accountable for the technical rigor of what we measure, learn, and report.

This is a founding role. The team, systems, and protocols do not exist yet, and you will design them. We are also looking for someone who pushes on how frontier AI can make MLE faster, cheaper, and more rigorous, and who shows our team and the wider field what is possible.

You Will:

• Set MLE strategy and standards: Define 2AI's approach to measurement and evaluation across Agriculture, Health, and the Incubator, and hold a consistently high bar for rigor from design through dissemination, including minimum standards for how we act on weak or emerging evidence, uncertainty, and key assumptions.
• Push the frontier on AI-enabled approaches to MLE: Test and create industry best practices for AI-enabled MLE approaches, and share learnings with the sector. Explore frontier AI tools and techniques, such as LLM-assisted evidence synthesis and agentic analysis pipelines, to improve the quality, speed, and cost of evaluation.
• Build and lead the MLE team: Design the team structure, recruit the first hires, and develop them into a high-performing group.
• Set a clear performance bar and build a culture of active feedback.
• Provide technical supervision and mentorship on diagnosing and solving complex problems.
• Assess the evidence base for early-stage programs and provide advisory support to Incubator, 2AI’s new program development unit (~30% of your time): Strengthen rigor and consistency as we assess and select new interventions.
• Review existing evidence with the Incubator team and stress-test hypotheses.
• Serve as a thought partner to the Incubator team on program assessment and pilot design, identifying where 2AI’s program design aligns with or departs from the existing evidence and ensuring pilots are designed to be measurable and focused on the most important learning goals.
• Identify evidence gaps and develop stage-appropriate learning plans, recognizing the limited evidence base for AI-enabled interventions.
• Review outcome and cost-effectiveness models: Pressure-test cost effectiveness analyses built by the Incubator team, checking how evidence feeds into key assumptions and whether results pass a reality check.
• Build measurement systems for live programs: Design protocols and data collection processes grounded in operational realities, and ensure rigorous iteration as Agriculture and Health programs scale. Build rapid experimentation, such as A/B testing of AI tool features and delivery approaches, into live programs so teams can learn and iterate quickly.
• Drive data-driven decision-making: Identify the highest-value questions across the portfolio, and make sure MLE findings reach the program and country teams making decisions on the ground.
• Own the MLE budget and resourcing plan: Plan resources for a new function, make tradeoffs aligned with organizational priorities, and secure the buy-in and funding to sustain it.
• Represent 2AI's evidence externally: Communicate methods, findings, and learning to funders and partners, invite and address external feedback, and publish MLE work as public goods.