AI Data Annotator Data Labeler

Job Title AI Data Annotator / Data Labeler Role Summary

We are looking for a highly detail-oriented Data Annotator responsible for labeling and curating datasets used to train and evaluate machine learning models.

This is not a passive “click-through” role.

You are expected to understand annotation guidelines deeply, apply consistent judgment, and maintain high-quality standards across large datasets.

Your work directly impacts model performance.

Key Responsibilities Data Annotation & Labeling Accurately label data across multiple modalities:

● Images (classification, bounding boxes, segmentation)

● Text (classification, entity recognition, sentiment, intent)

● Audio (transcription, tagging)

● Video (frame-level or sequence labeling)

Follow detailed annotation guidelines and schemas

Apply consistent decision-making across edge cases

Maintain high precision and recall in labeling tasks

Flag ambiguous or unclear cases instead of guessing

Quality Control & Consistency

Ensure labeling accuracy and consistency across datasets

Participate in review and audit processes (peer review or QA)

Identify labeling errors and correct them proactively Track and reduce:

● Label noise

● Inconsistencies

● Annotation drift over time Follow versioned guidelines and adapt to updates quickly Guideline Interpretation & Feedback Understand annotation instructions beyond surface-level reading Ask for clarification when guidelines are ambiguous or conflicting Provide feedback to improve:

● Label definitions

● Edge case handling

● Annotation workflows Help refine taxonomy and labeling schemas when needed

Data Handling & Workflow Work with annotation tools and platforms (Labelbox, CVAT, etc.)

Manage large volumes of data efficiently without sacrificing quality

Maintain structured workflows and task tracking

Handle sensitive or domain-specific data responsibly Collaboration with ML Teams Work closely with ML engineers and data teams Understand how labeled data is used in training and evaluation

Adapt labeling based on model feedback and failure cases

Support dataset curation and iteration cycles

Required Skills & Experience Core Annotation Skills Strong attention to detail — this is non-negotiable Ability to follow complex instructions precisely

Consistency in repetitive tasks without degradation in quality

Critical thinking for handling ambiguous cases

Experience Requirement Minimum 1 year of hands-on experience in data annotation / data labeling is required Experience should include working with real-world datasets (image, text, audio, or video) under defined annotation guidelines.

Demonstrated ability to maintain accuracy and consistency across large-scale labeling tasks

Technical Familiarity

Basic understanding of:

1) Machine learning concepts (what labels are used for)

2) Common task types (classification, detection, NLP tasks)

Experience with annotation tools is a plus:

1) Labelbox

2) CVAT

3) Prodigy

4) Scale AI or similar platforms

Quality and reliability:

Ability to maintain high accuracy over long annotation sessions

Strong time management and task discipline

Willingness to redo work when quality standards are not met

Communication:

Clear communication when raising issues or ambiguities

Ability to document edge cases and decisions

Comfort working with structured feedback loops

What we care about

Speed without accuracy is useless

Inconsistent labeling destroys model performance

We care about precision , consistency and accountability