The Agency Fund
Design and build AI/ML evaluations and features for Agency Fund's tech platforms and partners (LLMs, NLP, recommendation systems, etc.).
Develop robust data pipelines and evaluation frameworks for model training and inference and post-deployment efficacy.
Work closely with behavioral scientists and researchers to translate insights into AI-powered features.
Architect and maintain production ML systems with an emphasis on observability, performance, reliability and scalability.
Conduct rapid prototyping and experimentation to validate AI approaches before full buildout.
Document technical decisions and maintain engineering standards across AI components.
Stay current with applied AI research and bring relevant advances to the team.
You might be a great fit if you:
Have 3–7 years of software engineering experience with at least 2 years focused on AI/ML systems
Are proficient in Python and have hands-on experience with AI/ML frameworks (PyTorch, TensorFlow, HuggingFace, etc.)
Have built and deployed LLM-powered applications or retrieval-augmented generation (RAG) systems
Understand ML fundamentals: model training, evaluation, fine-tuning, and inference optimization
Are comfortable with cloud infrastructure (AWS, GCP, or Azure) and have deployed models in production
Communicate technical concepts clearly to non-engineering audiences
Care deeply about building AI responsibly and with direct social benefit
Bonus points if you have:
Experience with multilingual NLP or working with low-resource languages
Familiarity with behavioral science or designing AI for behavior change
Previous work in global health, international development, or social impact technology
Experience with data annotation pipelines and evaluation methodology for LLMs
Background in AI safety, alignment, or responsible AI frameworks
Why Join Us
Work with a kind, talented, and mission-driven team
Collaborate with bold thinkers across social impact, data, and product innovation
Shape projects that directly expand human agency
Flexible working environment
Competitive compensation commensurate with experience