Data Quality Manager

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. Data quality is one of the most important and least solved problems in humanoid robotics. The standards, tooling, and methodologies that worked for prior generations of AI, image classification, language, autonomous driving, don’t map cleanly onto the multimodal, embodied, sensor-rich data that Helix and our other AI systems learn from. We are looking for a Data Quality Manager to own data quality at Figure end-to-end — setting the standards, building the tooling, and leading the team that produces the training data behind Helix and our other AI systems. This is not a labeling-team management role. We’re looking for a strategic operator and thought partner who will define what the next generation of data quality and annotation looks like for the humanoid robotics industry. Responsibilities: Own data quality broadly at Figure — setting the strategy, standards, and operating model for how training data is produced, evaluated, and improved across all of our AI systems Own data quality metrics, including accuracy, consistency, rework rates, and guideline adherence across all labeling projects Define the standards, guidelines, QA methodologies, and audit processes for humanoid robot training data, effectively writing the playbook for an annotation discipline that doesn’t yet exist outside of a handful of frontier robotics labs Serve as a thought partner to the Helix team across all aspects of AI model development, helping shape what data we collect, how we evaluate it, and how data quality decisions feed into model behavior 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 Partner with engineering to design and develop new internal tooling for annotation, QA, and data review Requirements: 8-10+ 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 large-scale data quality or annotation 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 Bonus Qualifications: 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 The US base salary range for this full-time position is between $140,000 – $200,000 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.

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