Product Manager, Privacy, Rights & Trust

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’re hiring a Product Manager - Privacy, Rights & Trust to own a high-impact, 0-to-1 product value stream at Protege. This is a senior product role responsible for the vision, roadmap, and execution of our privacy and data trust capabilities. The scope spans three interconnected problems: translating research into product, building the right vendor and partner network, and ensuring what we build is credible with customers, data partners, and third-party experts. Success in this role means choosing the right first wedge, scoping it with discipline, and building a product direction that is not just functional, but trusted. What You’ll Do Own the privacy product from discovery through execution - Drive discovery to identify where Protege can create the most meaningful privacy and trust capabilities across different data types and customer contexts. - Synthesize signals from customers, data partners, and internal teams to define what should be built first and why. - Take the first wedge from problem definition to pilot-ready scope. - Build and manage the vendor and partner network - Maintain a current view of the privacy-preserving tooling and partner landscape, including strengths, limits, cost, and modality fit. - Use that landscape knowledge to make principled build-versus-buy decisions rather than defaulting to familiar tools or one-off requests. - Structure external relationships in a way that preserves flexibility as requirements evolve. - Translate research into product - Partner closely with Data Lab to identify which privacy-related research directions have real near-term product potential. Decide what is robust enough to operationalize, what needs more validation, and what is promising but not yet product-ready. - Build the bridge from proof-of-concept to a capability we can deliver at scale. - Navigate a complex external stakeholder map - Build credibility with customers, data partners, and third-party certifiers who care about how data is handled and what claims Protege can responsibly make. Communicate clearly to different audiences without over-claiming or talking past their real concerns. - Bring external feedback back into product scope in a form that helps the team make better decisions. - Make and defend scoping calls - Hold disciplined focus in a problem space that can expand infinitely across modalities, regulations, and risk tolerances. Know which problem to solve first, make that case clearly, and resist broadening scope before the foundation is proven. What Success Looks Like 30 days: Define the problem space - Build a working picture of the most promising privacy opportunities, the current research landscape, and the external stakeholders that matter. - Create early clarity on what first wedge is most credible and why. 60 days: Choose the first wedge - Drive toward a focused build recommendation, with build-versus-buy logic, research-readiness assessment, and clear scope boundaries. - Show good judgment about what Protege should pursue now versus what should wait. 90 days: Operationalize the direction - Translate the chosen direction into an execution plan with metrics, dependencies, and a credible external narrative. - Establis