Founding Product Manager, Media

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.

The Opportunity

We’re looking for a Founding Product Manager to own product development in Protege’s media business. With a handful of media customers today (including some of the biggest foundation models) and a full pipeline, we’re looking for someone to take the reins from our Head of Product and deliver on maturing the product suite serving our media vertical. Product opportunities span data ingestion and delivery systems — the backbone of how we scale our media data catalog for AI training — as well as the development of UIs and possibly data products. It will be up to you to find opportunities, work with stakeholders to prioritize them, design solutions with engineering, and oversee their build and rollout.

Up first, you’ll bridge engineering, product, and partnerships to ensure that millions of hours of content flow seamlessly through Protege’s ingestion, normalization, and metadata enrichment pipelines. You’ll work cross-functionally to build a product roadmap that will help the media business scale and translate those opportunities into clear requirements for our engineering team, managing the development, QA, and launch cycles for those solutions.

What You’ll Do

• Define, prioritize, and execute the roadmap for Protege’s media business, starting with projects in data ingestion/processing and content visibility/searchability

• Work with external partners and internal teams to understand pain points and prioritize solutions

• Translate abstract problems (e.g., “partner ingestion bottlenecks”) into actionable, measurable engineering work

• Manage backlog, tradeoffs, and stakeholder communication with precision and transparency

• Understand how AI developers use media embeddings and metadata downstream, ensuring data quality aligns with use case needs

• As a founding team member: contribute to product culture, hiring, and process definition

Who You Are

• 4–7 years of experience in product management, with at least 1-2 years of experience PM-ing data-processing solutions (ingesting, cleaning, transforming, transcoding, delivering, etc)

• Product management experience in an early-stage startup

• Proven ability to work closely with engineers and data scientists to ship complex, multi-system products

• Excellent communication and cross-functional leadership skills — able to align business and technical teams around shared goals

• Structured thinker who thrives in ambiguity and is comfortable owning outcomes end-to-end

• Comfortable with early-stage constraints: limited resources, shifting priorities, wearing multiple hats

• Familiarity with media data — file formats, metadata, common delivery and storage mechanisms — or the desire and ability to become an expert quickly

Bonus if you have these experiences:

• Experience with media content management, transcoding, or machine learning pipelines

• Familiarity with tools like AWS S3, FFMPEG, TwelveLabs, or similar video AI platforms

• Exposure to retrieval, search, or embedding-based applications

Why Product at Protege

• Build products and help grow the product team at a company on a trajectory to support the entire AI market

• Collaborate with exceptional engineers, data scientists, and partners across the AI ecosystem

• Solve complex technical challenges around making AI-ready data accessible that directly impact the future of AI