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Mercor
New York, United States
Source: Mercor careers · View original posting
From Mercor's posting. “We” and “our” refer to the employer.
Mercor's mission is to organize human intelligence to power the AI economy. We're a leading AI data company, building the layer between human expertise and frontier models. Millions of domain experts on the platform are paid over $4 million per day to train frontier AI models. Mercor's APEX benchmark family measures AI's real-world impact on professional work.
Mercor Enterprise brings this same infrastructure to Fortune 500 companies: helping companies capture how their best people actually work, translating that expertise directly back into agents.
Mercor is creating a new category of work where expertise powers AI advancement. Achieving this requires an ambitious, fast-paced and deeply committed team. You’ll work alongside researchers, operators, and AI companies at the forefront of shaping the systems that are redefining society. Mercor is a profitable Series C company valued at $10 billion. We work in-person five days a week in our San Francisco, NYC, or London offices.
Frontier models increasingly depend on data that goes beyond text: images, video, audio, documents, and combinations of these modalities. Turning that raw material into useful data products is technically difficult. Each modality has different formats, quality failures, processing costs, privacy considerations, and review workflows. The final product still needs to be consistent, traceable, and trustworthy. Mercor’s Frontier Data Products team builds the systems that make these products possible.
As Tech Lead Manager, you will lead the team responsible for turning complex multimodal inputs into reliable, customer-ready data products at scale. This is a player-coach role, split roughly evenly between technical contribution and people leadership. You will write and review production code, own important architecture decisions, and help resolve the hardest production problems. You will also hire, coach, and organize a team that can operate with clear ownership and strong independent judgment.
This is a product-engineering leadership role. Applied ML is part of the system, but success is measured by the quality, reliability, and usefulness of the products delivered—not by research output alone
You will shape both the multimodal product architecture and the engineering team building it.
The technical challenge extends beyond moving large media files. The system must preserve context, relationships, provenance, and quality across different modalities and transformations.
Customer requirements and available models will evolve quickly. The architecture must support new products without requiring the team to rebuild the system for every use case.
The team’s work sits directly between complex real-world inputs and the data products delivered to frontier AI customers.
The team can launch support for a new modality or product without building an entirely separate system.
Product quality is measurable, explainable, and auditable across human and model-assisted workflows.
Large, long-running jobs are observable and recoverable, with failures detected before they affect customer deliveries.
Customer-specific work produces reusable capabilities that make the next product faster to build. - Engineers own substantial areas independently rather than depending on the manager for every technical decision.
The team improves delivery speed while maintaining clear standards for quality, reliability, privacy, and cost.
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