Skild AI

Senior Master Annotator

Bengaluru, India · Work style not stated

Posted over 6 months ago: check it’s still open

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About the role

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EmployerSkild AI
LocationBengaluru, India
Work styleWork style not stated
Employer posted7 months ago

Company Overview

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 Senior Master Annotator to 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

Show full description

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

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