Data Annotator

Seebot.ai; Amritsar; Remote; INR 180,000 - 300,000; Part time

Job summary

Seebot.ai is seeking a Data Annotation Specialist to help build high-quality datasets for computer vision. You will label images and videos, create bounding boxes and segmentation masks, and review annotations for accuracy to train and improve ML models.

You'll work remotely on contract projects within the Data Operations team, following project guidelines, delivering consistent results, and providing feedback to refine labeling processes as needed.

Qualifications

• Attention to detail with ability to spot inconsistencies.
• Ability to follow instructions and apply them consistently.
• Comfort with repetitive visual tasks while maintaining accuracy.
• Good judgment for unclear examples.
• Basic computer proficiency and web-based tools.
• Strong written communication skills.
• Ability to meet quality and productivity expectations.

Responsibilities

• Annotate images and video per guidelines.
• Create and review bounding boxes, polygons, segmentation masks, and classifications.
• Track objects across video frames.
• Review model-generated annotations and correct inaccuracies.
• Provide feedback to improve guidelines.
• Work with project leads and QA to resolve errors.

Skills

Attention to detail

Detail-oriented

Repetitive visual tasks

Written communication

Independent work

Team collaboration

Tools

Annotation platforms

Web-based tools

Quality assurance processes

Job description

Location: Remote

Employment Type: Contract

Department: Data Operations

About The Role

As a Data Annotation Specialist at Seebot, you will play a key role in building high-quality datasets for computer vision applications. You’ll work with images and video to accurately label, classify, track, and review visual data used to train, evaluate, and improve machine learning models.

Depending on the project, your work may include creating bounding boxes and segmentation masks, tracking objects across video frames, assigning classes or identities, reviewing model-generated annotations, and correcting difficult or low-confidence cases.

Your work directly contributes to the accuracy, consistency, and reliability of the computer vision systems our clients are building.

What you’ll do

• Annotate images and video according to project-specific guidelines.
• Create and review bounding boxes, polygons, segmentation masks, keypoints, classifications, and other annotation types.
• Track objects consistently across video frames.
• Assign labels, classes, attributes, or identities where required.
• Review model-generated annotations and correct inaccurate or incomplete results.
• Identify difficult, ambiguous, or unusual examples and escalat... when necessary.
• Perform quality checks on your own work and annotations completed by others.
• Follow defined labeling rules, quality standards, and delivery requirements.
• Maintain consistency across large datasets and long-running projects.
• Provide feedback when annotation guidelines or workflows need clarification.
• Work with project leads and QA teams to resolve recurring errors and improve annotation quality.

What we’re looking for

• Strong attention to detail and the ability to spot small inconsistencies.
• Ability to follow detailed instructions and apply them consistently.
• Comfortable working with repetitive visual tasks while maintaining accuracy.
• Good judgment when dealing with unclear or difficult examples.
• Basic computer proficiency and confidence working with web-based tools.
• Good written communication skills.
• Ability to meet quality and productivity expectations.
• Reliable, organized, and comfortable working independently as well as within a team.

Nice to have

• Previous experience with data annotation, image labeling, video annotation, or quality assurance.
• Familiarity with computer vision concepts such as object detection, segmentation, tracking, or classification.
• Experience using annotation platforms.
• Experience reviewing or correcting machine-generated annotations.
• Familiarity with working on large image or video datasets.

What success looks like

You consistently produce accurate annotations, follow project requirements, identify problems early, and help maintain the quality of the datasets moving through Seebot. Over time, you’ll also become familiar with more complex annotation workflows, model-assisted labeling, quality review, and edge-case handling.