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XDOF

Data pipelines and annotation for robotics

QC Manager

Business OperationsConfirmed live in the last 24 hours
Full-timeJakarta, IndonesiaNo salary listedIn-person

About the Job

Requirements

  • A bachelor's degree in Business, Engineering, Data, Management, or a related field is required.
  • Three to five years of experience in Quality Control, Quality Assurance, Audit, Operations, or a related field is required.
  • Experience managing a Quality Control or Quality Assurance team in a high-volume operational environment is required.
  • Experience creating and implementing standard operating procedures, quality standards, and Quality Control processes is required.
  • Fluency in spoken and written Mandarin Chinese is required.
  • A good command of spoken and written English is required.
  • Proficiency in Excel or Google Sheets and comfort working with operational data are required.

Responsibilities

  • Lead the Quality Control function for AI data collection and video auditing.
  • Develop and maintain standard operating procedures, quality standards, audit guidelines, and Quality Control processes.
  • Monitor auditor performance, accuracy, error rates, and overall quality metrics.
  • Conduct regular quality audits and identify recurring issues, gaps, and root causes.
  • Establish Quality Control 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 Quality Control team to achieve quality and operational targets.

Desired Qualifications

  • Experience in AI data collection, data annotation, video auditing, content moderation, or AI/ML operations is a strong plus.

About the Company

XDOF

Data pipelines and annotation for robotics

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.

Company size
1-10
Company stage
Series A
Total funding
$78.3M
Headquarters
Berkeley, CA
Founded
2024

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