XTN-8952774 | LEAD DATA ANNOTATOR

The Lead Data Annotator oversees the quality, consistency, and execution of annotation work across a team of Data Annotators. In addition to performing complex annotation tasks, this role is responsible for reviewing outputs, ensuring adherence to guidelines, and maintaining high data quality standards for machine learning model training.

The Lead Data Annotator acts as a key quality control and escalation point, identifying edge cases, inconsistencies, and annotation issues, and providing clear, actionable feedback to Operations and Engineering teams. They also support training and coaching of junior annotators, help refine annotation workflows, and ensure alignment across projects to improve overall data quality and robot learning performance.

This role plays a critical part in bridging execution and quality assurance, ensuring that annotated datasets are accurate, consistent, and suitable for advancing robotics system intelligence and autonomy.

• Health Insurance/HMO 
• Enjoy unlimited MadMax Coffee
• Diverse learning & growth opportunities
• Accessible Cloud HR platform (Sprout)
• Above standard leaves

I - Annotation Execution & Quality Assurance

• Perform high-accuracy review, labeling, and annotation of advanced robot-generated data, including images, videos, system logs, and tele-operation recordings to support machine learning model training; contribute to daily annotation work, especially for high-complexity and priority datasets;
• Ensure strict adherence to annotation guidelines, SOPs, and quality standards across all datasets and projects;
• Conduct quality assurance reviews of annotated outputs from Data Annotators to ensure accuracy, consistency, and compliance with standards;
• Identify edge cases, inconsistencies, ambiguities, and robot failure modes, and provide actionable feedback for escalation to Operations and Engineering teams;
• Validate data integrity and ensure datasets meet required quality standards for integration into machine learning pipelines supporting robot learning and autonomy;
• Monitor recurring errors and quality trends, and recommend corrective actions to improve annotation accuracy and system performance.

II - Coaching, Training, and Capability Development

• Provide coaching, guidance, and technical support to Data Annotators to improve accuracy, productivity, and compliance with standards;
• Conduct onboarding and training for new team members on tools, workflows, and guidelines;
• Translate complex requirements into clear, actionable instructions for the team;
• Collaborate with Operations Analysts to integrate process updates and workflow changes into training materials;
• Contribute to the development of coaching materials using real team performance insights, common errors, and quality gaps;
• Reinforce best practices, quality standards, and continuous improvement in annotation techniques;
• Serve as the first point of clarification for annotation-related questions;
• Ensure proper use of tools, platforms, and documentation standards.

III - People Management

• Oversee quality, consistency, and execution of annotation work across a team of Data Annotators, ensuring alignment with project standards;
• Manage day-to-day team coordination including workload distribution, task prioritization, and delivery tracking;
• Ensure consistent interpretation and application of annotation guidelines across the team;
• Provide real-time coordination, coaching, and workflow support to ensure smooth execution;
• Collaborate with Operations Analysts, Team Leads, and US-based engineering and technical counterparts to improve workflows and robot learning outcomes;
• Escalate systemic issues, tool gaps, and process inefficiencies affecting quality or delivery;
• Support continuous improvement of SOPs, tools, and annotation processes;
• Maintain accurate reporting of team output, quality trends, and operational insights;
• Ensure adherence to data security, confidentiality, compliance, and safety standards.

• Educational Background: Bachelor’s degree or at least 2 years of college/university education required. Experience in data annotation, QA, or team leadership is an advantage.
• Technical Aptitude: Comfortable using annotation tools, platforms, spreadsheets, dashboards, and other technology-based systems. Ability to quickly learn new tools, workflows, and updated guidelines. Exposure to robotics, AI, or machine learning environments is a plus but not required. Experience in data annotation, QA, operations, or team lead roles;
• Leadership & Team Management: Ability to guide and support team members to meet quality standards, deliverables, and prioritization requirements. Capable of working with Operations and technical teams to align on requirements. Experience in supporting onboarding and development of team members is an advantage.
• Attention to Detail: Strong accuracy and consistency in reviewing work and identifying errors, inconsistencies, and quality issues in datasets. Familiarity with quality assurance practices such as spot checks and guideline adherence is a strong edge.
• Analytical & Problem-Solving Skills:
  Ability to identify patterns, errors, and inconsistencies in data while working within defined standards. Able to recognize recurring issues, assess data problems, and escalate them clearly with sufficient context and structured recommendations for resolution.
• Work Environment Fit: Ability to thrive in fast-paced, technology-driven environments.
• Communication Skills: Ability to understand instructions and communicate feedback, issues, and updates in a clear and structured manner. Must be able to coordinate with team members, align with Operations, and escalate issues to technical stakeholders when needed.
• Other Essential Traits: Organized, detail-oriented, disciplined, patient, and consistent. Able to handle repetitive high-accuracy work while supporting team coordination and oversight responsibilities.

• Exposure to robotics, automation systems, AI training, or tele-operation environments;
• Experience with image/video labeling or structured data review;
• Basic understanding of machine learning workflows and annotation processes;
• Experience in gaming, simulation, or high-focus operational environments;
• Awareness of basic data security and confidentiality practices when handling sensitive information.