In A Nutshell
As a Gates AI Fellow, join a prestigious cohort applying AI for public good and gain career-defining experience, access to Gates’ powerful network, and exposure to senior leadership.
Responsibilities
Problem Identification & Scoping
- Identify, frame, and prioritize real-world challenges in collaboration with program teams, ensuring solutions are grounded in user needs and local realities.
Prototyping & Rapid Experimentation
- Prototype rapidly with modern LLM and AI tools to test feasibility, generate insights, and create lightweight demos.
- Document learnings from prototypes and translate them into actionable recommendations for scale or next steps.
Design and Deliver AI Solutions
- Design and implement AI/ML models to address challenges in global health, development, agriculture, diagnostics, or education.
- Conduct rigorous model evaluation and validation, ensuring accuracy, fairness, and real-world applicability.
- Prepare, curate, and manage datasets, ensuring data integrity, security, and ethical use.
- Develop high-quality, reliable software, applying strong engineering practices such as clean code, version control, and robust testing.
- Leverage large language models (LLMs), including deployment, fine-tuning, and adaptation to specific use cases.
Teaching & Capacity Building
- Develop and deliver workshops, guides, and playbooks to enable Foundation staff and partners to use LLM tools effectively in their own work.
- Mentor program teams and local partners in applying AI responsibly, with a focus on usability and adoption in LMIC contexts.
Collaborate Across Teams and Partners
- Apply interdisciplinary problem-solving skills to translate AI research into practical solutions across multiple domains.
- Communicate technical concepts effectively to both technical and non-technical stakeholders, enabling shared understanding and informed decision-making.
- Collaborate with cross-functional teams across disciplines and geographies to co-design and deliver impactful solutions.
- Facilitate workshops and training sessions to demystify AI tools, enabling teams to directly apply solutions to their workflows.
Advance Responsible and Ethical AI
- Embed equity, bias mitigation, and data privacy in all technical approaches.
- Ensure prototypes are designed with explainability in mind, making them accessible to non-technical stakeholders.
- Align AI solutions with the Foundation’s mission and global health and development goals.
- Share thought leadership by integrating prior academic, research, or professional experiences to advance the responsible and impactful use of AI.
Skillset
- Education: Candidates must hold a Bachelor’s or Master’s degree (in Computer Science, Data Science, Engineering, Applied Math or relevant field).
- AI Technology: Proficiency in ML/AI modeling, software engineering, model evaluation, LLM deployment, and data wrangling, with evidence of applied coursework, research, internships, or early professional experience.
- AI Applications: Demonstrated interest in applying AI to fields such as health systems, agriculture, diagnostics, or education, with enthusiasm and literacy to use modern AI tools safely and pragmatically (direct AI research experience not required).
- Collaborative Mindset: Strong teamwork and adaptability, with experience working cross-functionally in complex environments.
- Communication Skills: Demonstrated ability to clearly articulate technical concepts and recommendations to both technical and non-technical audiences.
- Mission-Driven Ethos: Deep motivation to apply AI responsibly for public good, with alignment to equitable development and global health impact.
- Builder and Teacher Mindset : Hands-on experience building prototypes with LLMs (e.g., using open-source tools or API-based platforms) and a willingness to share learnings openly. Ability to teach and enable others, not just deliver technical outputs.