Consultant, AI for Science – Knowledge Asset Design Product Development Division

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PATH is a global nonprofit dedicated to achieving health equity. With more than 40 years of experience forging multisector partnerships and with expertise in science, economics, technology, advocacy, and dozens of other specialties, PATH develops and scales up innovative solutions to the world’s most pressing health challenges. 

PATH leads a three-year programme, funded by the Gates Foundation, to test whether an AI co-scientist can help identify potential correlates of protection for vaccine development and shorten the path to licensing new and improved vaccines. The programme began with rotavirus and maternal RSV vaccination and will move to other diseases in its final phase.

• Briefing material for the advisers and experts
• A conceptual outline of the minimum viable knowledge asset
• A workshop design and a written synthesis of its conclusions
• An options paper to support the funding decision, which will form the basis of the programme's knowledge asset roadmap.

Required skills and experience:

• PhD, or equivalent research experience, in biomedical informatics, computational biology, bioinformatics or a related field.
• Hands-on experience designing or building structured biomedical knowledge resources, such as knowledge graphs, ontology-aligned databases, curated evidence tables or retrieval-augmented generation systems for scientific literature.
• Working familiarity with large language models and agentic systems, including how retrieval and tool access, for example through the Model Context Protocol, shape what a model produces.
• Experience running expert consultations, technical working groups or similar processes, and a record of synthesising their conclusions.
• Ideally, grounding in vaccine immunology or infectious disease sufficient to follow and contribute to technical discussions.

We know great candidates won't always meet every listed qualification.

Research shows that some groups, on average, are more likely to self-select out if they feel they don't meet all requirements. If you're excited about the role and think you'd be a good fit, we encourage you to apply.