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Mercor is an AI data company on a mission to organize human intelligence to power the AI economy. The company operates a platform where millions of domain experts train frontier AI models, generating over $4 million per day in payments. Mercor is a profitable Series C company valued at $10 billion, with offices in San Francisco, NYC, and London.
As Tech Lead Manager of Frontier Data Products, you will lead a team building systems that transform complex multimodal data (images, video, audio, documents, and combinations) into reliable, customer-ready data products at scale. This is a player-coach role split roughly evenly between hands-on technical contribution and people leadership.
You will set technical and product strategy for multimodal data products, designing systems that support the full lifecycle from initial inputs through processing, review, validation, versioning, and delivery. You'll create reusable abstractions across modalities while preserving quality, performance, and customer-specific differences. The role requires building quality systems combining automated evaluation, model-assisted checks, expert review, sampling, and adjudication. Every output must be traceable: what produced it, what changed, how it was evaluated, and why it was accepted.
You'll own reliability, performance, and cost of storage-heavy, compute-intensive, long-running workflows. Stay hands-on by writing production code, leading design reviews, and contributing directly to the team's most consequential technical work. Manage and develop engineers through clear expectations, frequent feedback, thoughtful delegation, and meaningful ownership. Recruit and onboard engineers who raise the team's technical and execution bar.
Partner with product, ML, operations, and customer-facing teams to turn new customer needs into durable product capabilities rather than one-off solutions. Success means the team can launch support for new modalities or products without rebuilding the system, product quality is measurable and auditable, large jobs are observable and recoverable, customer-specific work produces reusable capabilities, and engineers own substantial areas independently.
Required: experience managing strong engineering teams while remaining hands-on; deep experience designing and operating production backend, distributed, or data-intensive systems; experience with large unstructured data, asynchronous processing, metadata/versioning, or compute-heavy workflows; relevant experience in multimedia infrastructure, computer-vision data, video/audio processing, ML platforms, annotation systems, or human-in-the-loop products; strong judgment about platform boundaries; track record taking zero-to-one products through architecture, launch, and sustained production operation; ability to recruit, coach, and retain excellent engineers.
