About the role
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: Our Business Development Representative is responsible for building a high-quality pipeline across our different vertical business units through disciplined, insight-led outbound prospecting. Focusing on organizations that provide real-world data for model development, you’ll run targeted sequences, qualify opportunities rigorously, and be responsible for prospecting execution. You’ll also help test and validate new outreach hypotheses for new customer personas in close coordination with sales and the General Managers of each of the verticals. Key
Responsibilities: Build and maintain a focused ICP target list for your assigned verticals. Avoid random outreach. Run daily outbound sequences (email, LinkedIn, calls) to generate qualified meetings. Prospect into Protege’s core wedge in each vertical: high-value real-world data that enables AI model development, not generic vertical SaaS. Qualify fast (use case, data needed, timeline, stakeholders, success criteria) and disqualify quickly. Deliver clean handoffs with crisp notes that accelerate next steps. Partner with Marketing to iterate messaging and turn winning plays into a repeatable outbound engine. Keep pipeline hygiene tight and report what’s working weekly. Build fluency in each vertical’s specific data landscape and AI constraints (ie. licensing, privacy, de-identification expectations, provenance, evaluation