Protege is the trusted source for AI-ready, real world data and expertise at every stage of the AI lifecycle. Backed by a16z and CRV.
About the role
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. Found on 1752vc Careers, the job board for startup and VC roles.
What they're looking for
- Minimum 2 years in data science, applied statistics, ML engineering, or quantitative research, working with real, messy data
- Solid statistics: sampling, confidence intervals, measurement design and inter-rater agreement. You can explain why a sample is big enough, and what it can't tell you
- Strong background, and experience using language models or AI coding tools to move fast
- Healthy skepticism. You verify claims before trusting them, including your own
- Clear writing for technical and non-technical readers, including external reviewers
- Authorization to work in the U.S. as a W2 employee (required for access to protected health information)
More about this role
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.
Healthcare data only becomes AI training data once patient privacy is protected to a standard an independent expert will certify, while keeping the clinical detail that makes it worth training on. Black out too little and patients are exposed. Black out too much and the data loses its value.
No single tool gets this right, because every dataset is different. A radiology report, a handwritten prior-authorization fax and a...
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