AI/ML Engineer

Somewhere in East Africa right now, a field worker is getting coached by an AI trainer on how to run a behavior change program. In South Asia, a nurse is consulting a clinical copilot mid-shift. A smallholder farmer is asking a chatbot whether to plant this week.

The Agency Fund (TAF) builds the AI systems behind those interactions — and then works to make sure they actually work for the people using them. We're a US non-profit that embeds engineers and product managers directly inside evidence-based nonprofits across East Africa, South Asia, and beyond, helping them build data infrastructure, run experiments, and scale impact through AI and product thinking.

Our team of 22 combines expertise in software, AI, social psychology, and development economics. Our community of 200+ partner organizations reaches millions of people annually. We're entrepreneurial, flat, and operate with minimal hierarchy — everyone contributes to a shared mission in ways that are hard to replicate elsewhere.

We're looking for an AI/ML Engineer to join our team. You'll design, build, evaluate, deploy and improve AI/ML systems that power Agency Fund's platforms and partner applications, like evaluation infrastructure for non-profits, AI coaches for training field workers, copilots to support nurses and doctors, chatbots that provide advisory to farmers, and more.

You will serve as an ML expert for the non-profits we fund and the partners we collaborate with, helping translate the best practices from AI research, especially in evaluating AI products, into production in the form of tools, playbooks (like https://eval.playbook.org.ai) and frameworks. Your work will directly improve outcomes for millions of people served by our NGO partners.

• Contribute to tools supported by The Agency Fund like Calibrate, an open-source AI evaluation infrastructure for non-profits.

• Work closely with behavioral scientists and researchers to identify common problems and translate solutions/learnings into reusable tools, playbooks, frameworks and publications.

• Open-source any non-trivial innovations that come out of our in-kind work.

• Ship fast by implementing rapid development and deployment cycles to deliver solutions efficiently and iteratively.

• Document technical decisions and maintain engineering standards across AI components

• Stay current with applied AI research and bring relevant advances to the team.

• Provide advice and support to NGOs building generative AI products.

• Be a mentor to NGOs, demonstrating industry best practices, culture, and tooling to the staff we may work with, helping to grow these capabilities from within as well.

• Participate in organizing workshops and seminars on sharing the lessons, best practices and tools that emerge from our work.

• Conduct site visits to observe NGO operations and draft recommendations to address technical needs.

• Be agentic by fostering a culture of proactive problem-solving and initiative-taking.

• Strong foundation in math, deep learning, LLMs and modern AI systems.

• Demonstrable proof of work: 4+ years of hands-on ML engineering experience where you have built, evaluated and systematically improved at least one end-user facing product powered by deep learning beyond integrating model APIs, or building internal dashboards or contributing to ML infra.

• At least 1–2 years of experience building LLM-powered applications, with at least one production-grade agent beyond proof-of-concept demonstrations.

• Hands-on experience evaluating AI systems with a deep understanding of dataset and evaluation design.

• Fluency with prompting and context engineering for agentic systems, and a clear sense of when the fix is a better prompt versus a better model, tool or pipeline.

• Must be very comfortable with programming in Python. This role is a very hands-on.

• Comfortable using coding agents to produce quality work, not AI slop. You take full accountability for what you ship with minimal need for additional verification.

• Comfort with reading research papers and quickly testing relevant ideas.

• Experimental mindset. You reason carefully about data, model and evaluation together, and make iterative, measurable progress rather than purely chasing hunches.

• Ability to think from first principles and design practical, scalable solutions.

• You care about making sure the product works for the intended users first. Any research artifact is a welcome side effect, not the goal.

• You identify what needs to be done and do it without waiting to be told.

• You take ownership and show urgency to see things through till the end

• Detail-oriented with a keen eye for spotting mistakes early.

• Strong written and verbal communication skills.

• Ability to communicate technical concepts clearly to non-engineering audiences

• Care deeply about building AI responsibly and with direct social benefit

• Experience with multilingual NLP or working wit