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.