Data Quality Partner Lead

Figure AI Figure AI · Robotics · HQ · Data Collection

Figure AI is seeking a Data Quality Partner Lead to establish and manage their external annotation and review vendor network. This role involves sourcing, evaluating, onboarding, and overseeing BPOs, labeling firms, and crowdsourcing platforms to extend data quality capacity for their AI Robotics company. The lead will translate internal data quality standards into vendor specifications and establish an operating model for managing partners at scale.

What you'd actually do

  1. Own Figure’s external annotation and review vendor strategy end to end, from sourcing through onboarding through ongoing performance management
  2. Build the vendor pipeline from zero by identifying and engaging BPOs, specialized labeling firms, crowdsourcing platforms, and emerging providers in the AI data ecosystem
  3. Run structured evaluations of prospective vendors, including pilots, quality benchmarking, and capacity testing, and make clear recommendations on who to scale with
  4. Translate the Data Quality team’s standards and methodologies into vendor specs, making sure the bar we set internally is the bar our partners deliver against
  5. Stand up the operating model for managing partners at scale, including scorecards, escalation paths, and the tooling

Skills

Required

  • 5+ years in vendor management, BPO/outsourcing partnerships, strategic sourcing, or operations roles with significant external partner ownership
  • Track record of building or scaling a vendor function from an early stage, ideally in a domain where quality was the primary lever
  • Excellent written and verbal communication skills, especially when setting expectations with external partners
  • Comfortable operating with ambiguity and managing multiple concurrent partner relationships
  • Low ego, team player with can-do attitude

Nice to have

  • Direct experience managing data annotation, labeling, or content review vendors
  • Background at a frontier AI lab, autonomous vehicle company, or other organization where data quality at scale was a core problem
  • A passion for helping scale the deployment of learning humanoid robots
  • Comfortable rapidly building and deploying apps/agents using AI coding tools to build internal tools, dashboards, and trackers (zero technical academic background necessary)

What the JD emphasized

  • build vendor network from scratch
  • managing data annotation, labeling, or content review vendors
  • data quality at scale was a core problem