Data Annotation Analyst

Jobgether; Saudi Arabia; Remote; SAR 67,000 - 134,000; Full time

Benefits offered by this job

Remote work flexibility

Company-provisioned machine

Flexible work environment

Job summary

Jobgether is seeking a Data Annotation Analyst based in Saudi Arabia to annotate 2D and 3D objects across images, video, and LiDAR data for autonomous systems. The role emphasizes precision, adherence to guidelines, and collaboration with QA teams to improve annotation guidelines and datasets.

The candidate will work with specialized tools to label vehicles, pedestrians, and environmental features, contributing to accurate perception models, while maintaining high production quality and

Qualifications

• Strong attention to detail and the ability to maintain accuracy during visually intensive work.
• Strong spatial reasoning skills to interpret 3D environments and relationships.
• Ability to learn and navigate specialized annotation software efficiently.
• Experience with image annotation or GenAI data workflows.

Responsibilities

• Annotate 2D and 3D objects across images, video, and LiDAR point clouds.
• Create and maintain segmentation, tracking, pose, and keypoint annotations.
• Follow detailed annotation specifications and QC feedback to meet quality standards.
• Identify ambiguous scenes and report tooling or specification gaps.

Skills

Attention to detail

Spatial reasoning

Software navigation

Data annotation tools

Education

Associate or Bachelor's degree

Tools

AutoCAD

3D annotation tools

Annotation software

Job description

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Data Annotation Analyst based in Saudi Arabia.

As a Data Annotation Analyst, you will help create high-quality ground-truth datasets used to train and validate perception models for autonomous and operator-assisted machines.

You will work with camera imagery, video, and LiDAR point clouds to identify and precisely label objects, people, terrain, and environmental features.

The role combines detailed visual analysis with specialized annotation tools, requiring accuracy, consistency, and strong spatial reasoning.

You will contribute directly to datasets that support advanced AI and machine perception systems operating in complex real-world environments.

Success requires maintaining high quality and productivity while following detailed annotation specifications and handling ambiguous scenarios thoughtfully.

You will collaborate with QA teams and fellow annotators through calibration sessions, guideline reviews, and regular feedback.

This is an opportunity to contribute to sophisticated AI applications while developing expertise in computer vision data and 3D spatial annotation.

Accountabilities:

• Annotate 2D and 3D objects across images, video, and LiDAR point clouds, including vehicles, machinery, people, signage, and environmental features.
• Create and maintain accurate segmentation, object tracking, pose, and keypoint annotations across visual sequences.
• Apply detailed annotation specifications covering class definitions, occlusion rules, object thresholds, and inclusion or exclusion criteria.
• Review completed work, respond to QA feedback, and consistently meet established quality and accuracy standards.
• Identify and escal ambiguous scenes, sensor artifacts, tooling problems, and gaps in annotation specifications rather than making assumptions.
• Document data quality and tooling issues clearly, including relevant details and reproducible steps.
• Participate in calibration sessions, guideline reviews, and team standups to maintain consistent interpretations across the annotation team.
• Share observations and edge cases that can improve annotation guidelines, taxonomies, and documentation.
• Maintain expected production volume while ensuring annotations remain accurate, consistent, and reliable.

Requirements:

• Strong attention to detail and the ability to maintain accuracy during repetitive, visually intensive work over extended periods.
• Strong spatial reasoning skills, including the ability to interpret 3D environments and understand object size, position, orientation, and relationships.
• Ability to learn and navigate specialized software efficiently, including keyboard shortcuts and other productivity features.
• Experience working in a fast-paced, scaled environment with defined productivity, quality, or accuracy targets.
• Experience with image annotation or GenAI data workflows.
• Reliable high-speed internet and a distraction-free workspace suitable for focused, visually intensive work.
• Ability to work on a company-provided machine within a controlled technical environment.
• Associate or bachelor's degree is a plus.
• Experience with image, video, LiDAR, or 3D annotation tools is preferred.
• Background in subsurface utility engineering, construction surveying, surveying, GIS, or other fields involving spatial interpretation of physical environments is advantageous.
• Experience with AutoCAD or similar technical design tools is a plus.
• Experience in construction, mining, agriculture, industrial operations, or heavy equipment environments is beneficial.
• Familiarity with point cloud data, sensor fusion, or camera-LiDAR calibration is preferred.
• Experience working with autonomous vehicles, robotics, or machine perception datasets is an advantage.

Benefits:

• Opportunity to contribute to advanced AI and computer vision applications across complex real-world environments.
• Flexible working environment designed to support different working styles and team needs.
• Access to specialized tools, resources, and opportunities to develop technical capabilities.
• Collaborative culture focused on innovation, accountability, curiosity, and high-quality AI solutions.
• Opportunity to work alongside experienced professionals on challenging AI training and evaluation projects.
• Remote work environment with the flexibility to work from a suitable location in the United States.
• Company-provisioned machine and controlled work environment for performing annotation tasks.