Remote Data Annotation Analyst — 2D/3D AI Vision

Jobgether

United States

Remote

MXN 180,000 - 300,000

Full time

14 days+

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Benefits offered by this job

Remote work with US accessibility

Company-provisioned machine

Flexible working environment

Collaborative culture

Job summary

Jobgether, on behalf of a partner company, seeks a Data Annotation Analyst based in Mexico to help create high-quality ground-truth datasets for autonomous and operator-assisted machines. You will label 2D/3D objects in images, video, and LiDAR, following detailed guidelines and collaborating with QA to ensure accuracy.

You will contribute to AI perception projects, working remotely with a company-provisioned machine.

Qualifications

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 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 is advantageous.
Experience with AutoCAD or similar tools.
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.

Responsibilities

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 escape 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.