AI/ML Engineer

Accountabilities:

In this role, you will build and improve production-ready AI systems while helping nonprofit partners adopt effective, measurable, and responsible AI practices. Your work will combine hands-on engineering, applied research, experimentation, and collaboration with teams working directly in the field.

• Design, build, evaluate, deploy, and improve machine learning systems and AI-powered applications for real-world use cases.

• Contribute to open-source AI evaluation infrastructure and tools that help nonprofit organizations assess and improve their AI products.

• Work closely with behavioral scientists, researchers, and field teams to identify recurring challenges and translate findings into reusable tools, frameworks, playbooks, and technical publications.

• Develop rigorous evaluation methods for AI systems, including appropriate datasets, evaluation criteria, and measurable performance improvements.

• Build and iterate on production-grade LLM applications and agentic systems, improving their reliability, usefulness, and performance for intended users.

• Release meaningful technical innovations from partner projects as open-source contributions whenever appropriate.

• Deliver solutions through rapid development and deployment cycles, using experimentation and measurable feedback to guide improvements.

• Document technical decisions, maintain engineering standards, and ensure AI components remain reliable and maintainable.

• Monitor developments in applied AI research, assess relevant advances, and test promising approaches in practical settings.

• Advise and support nonprofit organizations developing generative AI products, helping them adopt effective engineering practices, tools, and workflows.

• Mentor partner organization staff and help strengthen their internal technical capabilities and responsible AI practices.

• Contribute to workshops, seminars, and knowledge-sharing initiatives that communicate lessons and best practices from ongoing projects.

• Conduct site visits to understand operational challenges, observe how AI tools are used, and recommend technical improvements based on direct user needs.

• Take initiative in identifying problems, defining solutions, and driving projects through implementation and deployment.

Requirements:

The ideal candidate combines strong machine learning fundamentals with substantial hands-on experience delivering end-user AI products. You should be comfortable moving between research, experimentation, production engineering, and collaboration with nontechnical stakeholders, while maintaining a strong focus on practical impact.

• Strong foundations in mathematics, deep learning, large language models (LLMs), and modern AI systems.

• At least 4 years of hands-on machine learning engineering experience, including building, evaluating, and systematically improving at least one end-user-facing product powered by deep learning. Experience limited to integrating model APIs, building internal dashboards, or contributing solely to ML infrastructure does not meet this requirement.

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

• Practical experience evaluating AI systems, with a strong understanding of dataset design, evaluation methodology, and performance measurement.

• Proficiency in Python and a willingness to remain deeply involved in hands-on implementation.

• Fluency in prompting and context engineering for agentic systems, including the ability to determine whether a problem requires improved prompting, a different model, better tools, or changes to the overall pipeline.

• Experience using AI coding agents to produce high-quality work, with the judgment and accountability to validate outputs and take responsibility for what you ship.

• Ability to read research papers, assess new ideas, and quickly test relevant approaches through practical experimentation.

• An experimental mindset, with the ability to reason carefully about data, models, and evaluation while making iterative, measurable progress.

• Strong first-principles thinking and the ability to design practical, scalable solutions.

• A user-focused approach that prioritizes whether a product works effectively for its intended audience.

• Proactive ownership, urgency, attention to detail, and the ability to carry projects through to completion without constant direction.

• Strong written and verbal communication skills, including the ability to explain technical concepts clearly to nonengineering audiences.

• A commitment to responsible AI development and technology that delivers meaningful social benefits.

Bonus qualifications:

• Experience with multilingual natural language processing or low-resource languages, particularly Swahili, Hindi, and languages spoken across East Africa and South Asia.

• Previous work in global health, international development, or social-impact technology.

• Familiarity with behavioral science or AI applications designed to support behavior change.

• Experience with data annotation pipelines and LLM evaluation methodologies.

• Knowledge of AI safety, alignment, or responsible AI frameworks.

• Experience deploying models in production and working with cloud infrastructure such as AWS, Google Cloud Platform, or Azure.

• Publications at leading AI conferences or substantial technical writing documenting engineering research and results.

Benefits:

• Compensation: Competitive within the mission-driven technology sector, with compensation positioned below typical big-tech salary levels.

• Remote work: Fully remote, asynchronous-first working environment.

• Flexible schedule: Work hours can accommodate your time zone, with no mandated core hours.

• International collaboration: Join a globally distributed team working across North America, Europe, East Africa, East Asia, and South Asia.

• Meaningful impact: Build AI applications addressing practical challenges in healthcare, agriculture, and other areas of international development.

• Technical ownership: Take projects from initial problem definition and model selection through deployment, evaluation, and continuous improvement.

• Interdisciplinary collaboration: Work alongside behavioral scientists, development economists, field researchers, and embedded engineers.

• Open-source contributions: Share technical innovations, tools, and learnings with the wider community rather than keeping all work within proprietary products.

• Professional development: Stay close to applied AI research and gain experience solving challenging problems in real operational environments.

• Autonomy and responsibility: Contribute within a small team with minimal hierarchy, direct feedback, and opportunities to influence technical decisions.

• Location eligibility: The role is open to candidates based in Sub-Saharan Africa and South Asia. India falls within the stated eligible regions, subject to the hiring team's location and contracting requirements.

• Contract arrangement: International hiring is conducted on a contractor basis.