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Protege is building a platform to solve AI's data problem by facilitating secure, efficient, and privacy-centric exchange of AI training data. The company is backed by world-class investors and powers partnerships with frontier labs and ambitious AI teams.
As an Applied Healthcare Researcher, you will join DataLab, a team focused entirely on healthcare training data. You'll work directly with researchers at frontier labs and AI startups building specialized healthcare models. Your core mission is to understand their model-development problems, determine which healthcare data can support those problems, and conduct research to demonstrate feasibility.
You will be the primary technical and research point of contact for healthcare customers—not a supporting resource, but the person driving conversations and pulling in solutions, engineering, and data partnerships teams as needed. This is fast-iterating, customer-facing research executed on the customer's timeline.
Key responsibilities include: serving as the primary technical partner for AI researchers working on healthcare problems; translating model-development goals into concrete data strategies; scoping opportunities and identifying high-value available data; explaining data limitations, tradeoffs, and biases to technically sophisticated stakeholders; and answering research questions about delivered data.
You will own the applied research and method development needed to curate the right data product. This includes developing and evaluating methods (fine-tuning, LLM-based extraction, classification, rules-based approaches) to demonstrate that datasets support training or evaluation objectives. You'll design and run feasibility research pre-contract, build evidence bases with benchmarks and validation analyses, and partner on healthcare benchmarks across modalities.
You'll evaluate whether requested variables, labels, or cohort definitions are achievable with available healthcare data; identify proxy variables when ideal variables don't exist; analyze partner and source datasets for schema, field availability, quality, and completeness; and contribute to organizational perspective on which healthcare data matters most for different modalities and development stages.
The role emphasizes producing reusable research and technical collateral, identifying where one-off approaches should become repeatable workflows, and helping expand proven datasets across multiple customers. You'll collaborate cross-functionally with Solutions, Field Development Engineers, Healthcare Data Partnerships, Product, and Engineering teams from the beginning of opportunities.
