QC Manager
XDOF
QC Manager
XDOF
Data pipelines and annotation for robotics
Job Description
Lead the Quality Control function for AI data collection, and video auditing
Develop and maintain SOPs, quality standards, audit guidelines, and QC processes.
Monitor auditor performance, accuracy, error rates, and overall quality metrics.
Conduct regular quality audits and identify recurring issues, gaps, and root causes.
Establish QC scoring systems, checklists, and quality evaluation frameworks.
Provide feedback, coaching, and calibration sessions to improve team accuracy and consistency.
Collaborate with Operations, Training, and other teams to resolve quality issues and improve processes.
Prepare quality reports and provide data-driven recommendations to management.
Lead and develop the QC team to achieve quality and operational targets.
Job Requirements
Bachelor’s degree in Business, Engineering, Data, Management, or a related field.
3–5 years of experience in Quality Control, Quality Assurance, Audit, Operations, or related fields.
Experience managing a QC/QA team in a high-volume operational environment.
Proven experience creating and implementing SOPs, quality standards, and QC processes.
Strong analytical, problem-solving, and attention-to-detail skills.
Strong leadership and communication skills.
Fluent in Mandarin Chinese (spoken and written) — MUST.
Good command of English, both spoken and written.
Proficient in Excel/Google Sheets and comfortable working with operational data.
Experience in AI data collection, data annotation, video auditing, content moderation, or AI/ML operations is a strong plus.
XDOF builds data pipelines, data-collection tools, and annotation systems that frontier AI labs and robotics companies use to train robots to operate in the physical world. It tackles the data bottleneck in physical AI by combining teleoperation, simulation, and egocentric data collection, then layering in data cleaning, tooling, and annotation to prepare usable datasets. The company differentiates itself through its end-to-end data infrastructure for physical AI, its mix of teleoperation, simulation, and first-person data methods, and its Berkeley-founded background from 2024. Its goal is to enable robots to learn to operate in the real world by providing the pipelines and labels needed for training.
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