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 We’re hiring a Solutions Engineer for our media vertical to connect Protege’s media catalog with customer AI data needs. This is not a traditional modeling role. It is an applied data curation and delivery role for fast-moving, ambiguous environments where both speed and quality matter. You will work with imperfect, evolving partner datasets and build strategies to normalize, validate, and operationalize them for downstream AI use cases. You’ll become an expert in Protege’s growing catalog of audio, video, and motion capture content — from longform assets with title-level metadata to clip-level content generated with TwelveLabs embeddings. At a high level, you will understand what customers are building, identify the content that best fits their needs, and deliver datasets that meet both technical and conceptual
requirements, often on tight timelines tied to active deals. What You’ll Do Own data quality and curate media datasets Partner with Sales and Solutions to translate customer
requirements into curation strategies Work with imperfect partner data, including mismatched metadata, schema differences, and incomplete labeling Normalize and standardize datasets for reliable downstream use Query and analyze Protege’s media catalog using SQL, internal APIs, and metadata tools to identify relevant content Build validation checks and workflows to ensure dataset integrity before delivery Identify, debug, and resolve data quality issues across file structures, metadata, and content alignment Use AI tools and transcoded embeddings to surface and refine clip-level content Turn messy, real-world data into structured datasets that meet customer and model