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Manage the operational delivery of human data for a strategic AI customer. Responsibilities include translating requirements into workflows, managing participant pools and onboarding, tracking supply and quality, leading cross-functional projects, resolving operational issues, documenting processes, and improving scalability with Product and Engineering. The role also supports participant assessment and certification programs while serving as an operational point of contact for delivery teams.

Leads complex AI client projects from requirements gathering through delivery, serving as the primary client contact and coordinating Sales, Operations, Product, and Engineering. Responsibilities include defining scopes and delivery plans, managing risks, quality, timelines, budgets, profitability, and KPIs, resolving execution challenges, and improving scalable processes for AI project delivery.

Partner with AI labs and enterprise teams on evaluation and human-data projects. Design methodologies, rubrics, data-collection approaches, quality frameworks, verifiers, validation systems, and reinforcement learning environments. Own projects from scoping through delivery, prototype tooling, advise customers, identify platform opportunities, collaborate with Product and Engineering, contribute to research publications, mentor colleagues, and communicate advanced AI concepts to diverse stakeholders.

Manage end-to-end operations for specialized participant pools supporting AI projects. Responsibilities include participant onboarding, verification, quality management, engagement monitoring, process optimization, project planning, documentation, and stakeholder enablement. Partner with product, technology, data, science, delivery, and customer success teams to scale manual workflows into technology-driven solutions and support high-quality human data collection for AI tasks.

Design and own end-to-end data quality systems for managed AI data studies: define rubrics, sampling plans, automated checks, launch gates, drift detection, dashboards, and calibration. Investigate integrity issues, run root-cause analysis, build automation in Python/SQL, and partner with Product, Engineering, and Operations to embed quality controls, train reviewers, and scale repeatable quality playbooks across programs.

The Participant Recruitment Partner will identify and engage top-tier participants, manage client-specific recruitment operations, and enhance the participant pool quality through data-driven insights and collaboration.