Senior Master Annotator

Skild; Bengaluru; Presencial; INR 1.200.000 - 1.800.000; Jornada completa

Descripción de la vacante

Skild is building advanced robotic annotation pipelines in Bengaluru, seeking a Senior Master Annotator to define and maintain gold-standard datasets and ensure high-quality labeling for robotics model training. You will lead IAA initiatives, calibrate annotators, and drive quality through structured guidelines and regular QA audits.

The role requires strong expertise in image/video/3D/audio annotation, attention to detail, and the ability to translate SOPs into precise guidelines.

Formación

• 4–7+ years of experience in data annotation, quality control, or video labeling environments.
• Strong expertise in temporal video annotation; robotics, computer vision, or autonomous systems datasets preferred.
• Deep understanding of Inter-Annotator Agreement methodologies (e.g., Cohen’s/Fleiss’ kappa), quality metrics, sampling strategies, and gold set creation
• Experience mentoring, training, or guiding annotators to improve accuracy and consistency
• Strong analytical skills with proficiency in Excel or Google Sheets (Python preferred but not required)
• Ability to translate ambiguous SOPs into precise, actionable annotation guidelines
• Exceptional attention to detail and sound judgment in ambiguous visual tasks
• Data-driven, process-oriented mindset with strong ownership and accountability
• Excellent written and verbal communication skills

Responsabilidades

• Define and maintain gold-standard annotations and benchmark (“gold set”) datasets for new and existing tasks
• Establish clear acceptance criteria, tolerance thresholds, and task-specific quality standards (e.g., frame-level tolerances)
• Develop, document, version-control, and continuously refine annotation guidelines and SOPs based on disagreement patterns and quality findings
• Design and implement Inter-Annotator Agreement (IAA) frameworks (e.g., Cohen’s/Fleiss’ kappa, tolerance-based agreement models) and produce regular quality reports
• Analyze disagreement trends, identify root causes, and recommend data-driven process improvements
• Lead onboarding and ongoing training for annotators, including structured calibration sessions
• Build and maintain edge-case libraries and example banks to improve annotation consistency
• Provide targeted feedback and retraining to improve annotator performance and alignment with gold standards
• Conduct precision-based QC audits according to task requirements and validate pilot batches prior to production scaling
• Review escalated edge cases and partner with QA and Operations teams to optimize sampling strategies and quality processes

Conocimientos

Temporal video annotation

Inter-Annotator Agreement (IAA)

Mentoring annotators

Data-driven process improvements

Attention to detail

Clear communication

Quality control

Herramientas

Excel / Google Sheets

Python (basic)

Descripción del empleo

At Skild AI, we are building the world's first general purpose robotic intelligence that is robust and adapts to unseen scenarios without failing. We believe massive scale through data-driven machine learning is the key to unlocking these capabilities for the widespread deployment of robots within society. Our team consists of individuals with varying levels of experience and backgrounds, from new graduates to domain experts. Relevant industry experience is important, but ultimately less so than your demonstrated abilities and attitude. We are looking for passionate individuals who are eager to explore uncharted waters and contribute to our innovative projects.

Position Overview

We are looking for a highly detail-oriented and technically strong SeniorMaster Annotatorto define, maintain, and continuously improve annotation standards for robotic datasets. Various type of data sets can be generated which needs to be annotated and quality checked before they are put in production

This role will act as the gold standard authority for labeling tasks, own Inter-Annotator Agreement (IAA) measurement, train and calibrate annotators, and ensure that annotation quality meets the high standards required for robotics model training.

The ideal candidate understands image, video, 3D and audio annotation, quality metrics, and how annotation accuracy impacts model performance.

Responsibilities

• Define and maintain gold-standard annotations and benchmark (“gold set”) datasets for new and existing tasks
• Establish clear acceptance criteria, tolerance thresholds, and task-specific quality standards (e.g., frame-level tolerances)
• Develop, document, version-control, and continuously refine annotation guidelines and SOPs based on disagreement patterns and quality findings
• Design and implement Inter-Annotator Agreement (IAA) frameworks (e.g., Cohen’s/Fleiss’ kappa, tolerance-based agreement models) and produce regular quality reports
• Analyze disagreement trends, identify root causes, and recommend data-driven process improvements
• Lead onboarding and ongoing training for annotators, including structured calibration sessions
• Build and maintain edge-case libraries and example banks to improve annotation consistency
• Provide targeted feedback and retraining to improve annotator performance and alignment with gold standards
• Conduct precision-based QC audits according to task requirements and validate pilot batches prior to production scaling
• Review escalated edge cases and partner with QA and Operations teams to optimize sampling strategies and quality processes

Preferred Qualifications

• 4–7+ years of experience in data annotation, quality control, or video labeling environments
• Strong expertise in temporal video annotation; experience with robotics, computer vision, or autonomous systems datasets preferred
• Deep understanding of Inter-Annotator Agreement methodologies (e.g., Cohen’s/Fleiss’ kappa), quality metrics, sampling strategies, and gold set creation
• Experience mentoring, training, or guiding annotators to improve accuracy and consistency
• Strong analytical skills with proficiency in Excel or Google Sheets (Python preferred but not required)
• Ability to translate ambiguous SOPs into precise, actionable annotation guidelines
• Exceptional attention to detail and sound judgment in ambiguous visual tasks
• Data-driven, process-oriented mindset with strong ownership and accountability
• Excellent written and verbal communication skills