Lead, Data Quality - Partnerships

Figure; San Jose (CA); On-site; USD 100,000 - 160,000; Full time

Job summary

Figure, an AI robotics company, is seeking an External Partnerships Lead in San Jose, CA to manage data quality vendor relationships with end-to-end accountability. You will source, onboard, and offboard labeling partners, negotiate terms, and enforce SLAs to ensure external output meets internal standards.

You will collaborate with engineering and ML teams to translate requirements into vendor guidelines, plan capacity, and drive regional/vendor expansion while guiding project coordinators and

Qualifications

• 6+ years in data quality, data labeling operations, vendor or partner management, or ML data operations.
• Direct experience selecting, contracting, and managing external data vendors, including SLAs.
• Track record negotiating commercial terms (pricing, SOWs, capacity) and running vendor reviews.
• Experience setting quality standards, acceptance criteria, and audit/calibration processes for external teams.
• Comfort working with engineering and ML stakeholders to translate technical requirements into operational guidance.
• Leadership across people you both do and do not directly manage, including coordinators working with vendors.

Responsibilities

• Own the full external vendor portfolio for Data Quality: sourcing, evaluation, onboarding, and offboarding of data labeling and review partners.
• Negotiate commercial terms, statements of work, and contracts, and manage pricing, budgets, and surge capacity across vendors.
• Set and enforce SLAs and acceptance criteria; hold every vendor to the same golden set and audit bar as the in-house team.
• Serve as the senior owner when a project rotates to external support, directing the Project Coordinators running that work.
• Build and run the vendor scorecard: throughput, quality, inter-annotator agreement, and SLA adherence, reviewed in regular business reviews.
• Stand up calibration exercises and golden set checks with each vendor so external output matches internal standards.
• Partner with engineering and ML stakeholders to translate data quality requirements into vendor-ready guidelines and specs.
• Plan capacity and ramp with vendors to meet project timelines, including surge needs and new regions.
• Coordinate with internal Project Coordinators and leadership on which projects rotate to which vendors and when.

Skills

Vendor management

Data quality

ML data operations

Leadership

Contract negotiation

Job description

Figure is an AI robotics company developing autonomous general-purpose humanoid robots. The goal of the company is to ship humanoid robots with human level intelligence. Its robots are engineered to perform a variety of tasks in the home and commercial markets. Figure is headquartered in San Jose, CA. We are building the data foundation that powers our humanoid robots.

The Data Quality team owns the standards, guidelines, and audit infrastructure that ensure our training data meets the bar our AI systems require. As we scale, a growing share of that work runs through external vendors, and this role owns those relationships end to end.

External Partnerships Lead

As External Partnerships Lead, you'll own Figure's external data vendor portfolio for Data Quality: who we work with, what we hold them to, and how their output meets the same bar as our in-house team. You'll source and select vendors, negotiate the commercials, set and enforce SLAs, and stand up the calibration and audit processes that keep external work on standard. When a project rotates to external support, you are the senior owner of that delivery, and the Project Coordinators on it work through you.

Responsibilities

• Own the full external vendor portfolio for Data Quality: sourcing, evaluation, onboarding, and offboarding of data labeling and review partners.
• Negotiate commercial terms, statements of work, and contracts, and manage pricing, budgets, and surge capacity across vendors.
• Set and enforce SLAs and acceptance criteria; hold every vendor to the same golden set and audit bar as the in-house team.
• Serve as the senior owner when a project rotates to external support, directing the Project Coordinators running that work.
• Build and run the vendor scorecard: throughput, quality, inter-annotator agreement, and SLA adherence, reviewed in regular business reviews.
• Stand up calibration exercises and golden set checks with each vendor so external output matches internal standards.
• Partner with engineering and ML stakeholders to translate data quality requirements into vendor-ready guidelines and specs.
• Plan capacity and ramp with vendors to meet project timelines, including surge needs and new regions.
• Coordinate with internal Project Coordinators and leadership on which projects rotate to which vendors and when.

Requirements

• 6+ years in data quality, data labeling operations, vendor or partner management, or ML data operations, with end-to-end ownership of vendor relationships or major workstreams.
• Direct experience selecting, contracting, and managing external data vendors, including defining and enforcing SLAs.
• A track record negotiating commercial terms (pricing, SOWs, capacity) and running vendor business reviews.
• Experience setting quality standards, acceptance criteria, and audit or calibration processes that external teams executed against.
• Comfort working with engineering and ML stakeholders to translate technical requirements into operational guidance.
• Leadership across people you both do and do not directly manage, including coordinators running work on your vendors.

Bonus Qualifications

• Experience scaling a multi-vendor or multi-region labeling operation.
• Experience with golden set / ground truth construction.
• Background in robotics, autonomous systems, LLM, or physical AI data.
• Experience standing up new vendor operations in international or lower-cost markets.

The US base salary range for this full-time position is between $100,000 and $160,000 annually. The pay offered for this position may vary based on several individual factors, including job-related knowledge, skills, and experience. The total compensation package may also include additional components/benefits depending on the specific role. This information will be shared if an employment offer is extended.