Tech Lead Manager, Frontier Data Products

About Mercor 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. About the Role 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 What You’ll Do Set the technical and product strategy for Mercor’s multimodal data products. Design systems that support the full lifecycle of image, video, audio, document, and mixed-media data—from initial inputs through processing, review, validation, versioning, and delivery. Create reusable abstractions across modalities while preserving the differences that matter for quality, performance, and customer requirements. Build quality systems that combine automated evaluation, model-assisted checks, expert review, sampling, and adjudication. Ensure that every output is traceable: what produced it, what changed, how it was evaluated, and why it was accepted. Own the reliability, performance, and cost of storage-heavy, compute-intensive, long-running workflows. Stay hands-on by writing production code, leading design reviews, and directly contributing to the team’s most consequential technical work. Manage and develop engineers through clear expectations, frequent feedback, thoughtful delegation, and meaningful ownership. Partner with product, ML, operations, and customer-facing teams to turn new customer needs into durable product capabilities rather than one-off solutions. Recruit and onboard engineers who raise the team’s technical and execution bar. What Makes This Role Different 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. What Success Looks Like 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 ca