Applied Healthcare Researcher

Company Overview:

We are building Protege to solve the biggest unmet need in AI — getting access to the right training data. The process today is time intensive, incredibly expensive, and often ends in failure. The Protege platform facilitates the secure, efficient, and privacy-centric exchange of AI training data.

Solving AI’s data problem is a generational opportunity. We’re backed by world-class investors and already powering partnerships with some of the most ambitious teams in AI. The company that succeeds will be one of the largest in AI — and in tech.

We’re a lean, fast-moving, high-trust team of builders who are obsessed with velocity and impact. Our culture is built for people who thrive on ambiguity, own outcomes, and want to shape the future of data and AI.

Role Overview

We are hiring Applied Healthcare Researchers to join a team within DataLab focused entirely on healthcare training data.

Our customers are researchers at the frontier labs and AI startups building specialized healthcare models. They come to us with model-development problems, not dataset specifications. Figuring out which healthcare data actually solves their problem, and proving that it does, is the research question we answer in DataLab.

In this role you will work directly with researchers at those labs to understand what they're trying to train or evaluate, determine what healthcare data can support it, and do the research needed to demonstrate that it will. This is fast-iterating, customer-facing research on a customer's timeline. You will be the primary technical and research link to the customer — not a technical resource brought in for credibility, but the person driving the conversation and pulling in the solutions, engineering, and data partnerships teams as needed.

Core Responsibilities

Customer Research Partnership

You will be the research partner to AI researchers at frontier labs and startups who are working on healthcare problems.

• Serve as the primary technical and research point of contact for healthcare customer conversations.
• Translate a lab's model-development goals into concrete, feasible data strategies.
• Help customers scope opportunities and identify the highest-value data available to them.
• Explain data limitations, tradeoffs, and potential biases to technically sophisticated stakeholders while grounding conversations in what real-world data actually looks like.
• After delivery, answer the research questions customers raise about the data we provided. Delivery is not the end of the relationship.

Applied Research & Method Development

Curating the right data product is a research problem, and you'll own solving it.

• Develop and evaluate methods — fine-tuning, LLM-based extraction, classification, rules-based approaches, or whatever the problem calls for — to demonstrate that a dataset can support a customer's training or evaluation objective.
• Design and run feasibility research pre-contract: can this data support this model objective, at what quality, with what caveats.
• Build the evidence base that makes a data strategy credible — benchmarks, validation analyses, error characterization, and honest assessments of where the data falls short.
• Partner with the Assessments team on healthcare benchmarks across modalities.

Data Feasibility & Dataset Strategy

• Evaluate whether requested variables, labels, or cohort definitions are achievable with available healthcare data.
• Identify proxy variables or alternative dataset structures when the ideal variable doesn't exist.
• Analyze partner and source datasets — schema, field availability, quality, completeness, and required transformations.
• Contribute to our point of view on which healthcare data matters most for which modality and which stage of model development.
• Help evaluate new data partners and identify datasets worth acquiring before a customer asks for them.

Reusable Research & Scaling

• Produce reusable research, evidence, and tec