Mercor Product Manager, Real World Data at Mercor to own how real-world data becomes datasets for AI labs. Define data usefulness, shape datasets, and accelerate delivery to labs.
Responsibilities
As a Product Manager for Real World Data, you will own how real-world data becomes datasets labs want to buy. You will define what makes this data useful at each stage of training, decide how datasets are shaped, packaged, and delivered, and get high-quality data into labs' hands faster. The role spans product and go-to-market: you will need to understand what is in the data, what labs need, and how to sell it. Working this close to real work, you will see firsthand where frontier models fall short on the tasks that matter most. Real-world data is a new category of product, and what you build will help define it and shape what the next generation of models learns from.
Build New Product Lines: Partner with research and environments teams to make real-world data useful for post-training, so agents learn the messy, judgment-heavy work they will face in real workplaces.
Own the Roadmap: Define success in terms of sellable datasets, and use lab feedback, sales outcomes, and unmet demand to decide what the team builds next.
Lead Product Discovery: Define what makes real-world data valuable for pre-training, mid-training, and post-training, and turn that understanding into dataset specifications, quality standards, and ways to measure impact on models.
Define Packaging and Positioning: Turn acquired data into off-the-shelf dataset products, deciding what is sellable, to whom, and in what form.
Set the Quality Bar: Set standards for how datasets are structured, documented, and delivered, and build the visibility to catch problems before delivery and trace each one to its source.
Improve Time to Value: Shorten the path from customer request to accepted delivery by tightening scoping, sampling, and quality review.
You might thrive in this role if you:
Experience: Have 3+ years as a product manager, prior experience in SWE, ML, or DS preferred.
High Ownership: Take full accountability for outcomes, not just outputs.
Independence: Are able to self-direct in ambiguous contexts, creating clarity for others.
Technical Fluency: Are comfortable building scrappy MVPs yourself when needed to validate direction, unblock engineering, or accelerate iteration.
