Data Quality Manager

Figure•San Jose, CA

21h

About The Position

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. Our Data Quality team is responsible for transforming raw sensor, image, and video data from our humanoid robots into high-quality labeled datasets used to train and evaluate our AI systems. This team operates at the critical intersection of robotics, machine learning, and operations, ensuring that our training data is accurate, consistent, and scalable as Helix evolves. We are looking for a Data Quality Manager to build and lead Figure’s Data Labeling team and own the quality, consistency, and throughput of labeled data used to train our AI systems.

Requirements

5-10 years of experience leading operational or data teams in a fast-paced environment, including hiring, performance management, and coaching
Strong analytical and problem-solving skills, with the ability to diagnose quality issues and implement corrective actions
Experience managing quality assurance processes or large-scale data operations
Excellent written and verbal communication skills, especially when documenting standards and providing feedback using data
Ability to manage competing priorities and time-sensitive deliverables under pressure
High attention to detail and a strong quality-first mindset
Proficiency in Google Workspace (e.g., Sheets) and operational or workflow management tools

Nice To Haves

Experience with AI data labeling, computer vision annotation, or ML dataset curation
Experience working with robotics, autonomy, or sensor-derived data
10+ years of experience leading skilled teams operating complex or early-stage technology
A passion for helping scale the deployment of learning humanoid robots

Responsibilities

Build, train, and lead Figure’s Data Quality team, providing direction, coaching, and performance management to ensure consistently high annotation quality and throughput
Own data quality metrics, including accuracy, consistency, rework rates, and guideline adherence across all labeling projects
Establish and continuously improve labeling guidelines, QA standards, audit processes, and escalation workflows
Manage day-to-day labeling operations, including workload planning, staffing coverage, and queue prioritization
Be the point of contact between the labeling team and ML engineers, translating model needs into clear labeling instructions and feedback loops
Develop onboarding and ongoing training programs for new and existing labelers
Review edge cases and ambiguous annotations, driving resolution and guideline updates in collaboration with the ML team
Implement process improvements and tooling enhancements to increase efficiency, reduce error rates, and scale output
Produce regular reporting on quality, productivity, and operational health for cross-functional stakeholders
Respond to urgent data quality issues, and supporting rapid turnaround labeling projects
Recruit, interview, and develop Data Labelers that can grow into Project Leads