Data Annotator (AI/ML Training Data)

We are looking for detail-oriented and reliable Data Annotators to create high-quality training data for AI and Machine Learning models. You will be responsible for accurately labeling images, videos, text, LiDAR, or other sensor data according to project guidelines. Your work plays a critical role in building AI systems, where accuracy and consistency directly impact model performance. Key Responsibilities Annotate datasets including images, videos, LiDAR, text, and other data types. Create accurate bounding boxes, polygons, segmentation masks, keypoints, object tracking, classifications, and other annotation types based on project requirements. Follow annotation guidelines consistently and accurately. Review your work before submission to minimize annotation errors. Report ambiguous cases, edge cases, or unclear guidelines to the QA team. Meet both productivity and quality targets while maintaining high accuracy. Adapt quickly to updated guidelines and project requirements. Collaborate with QA Specialists and Team Leads to continuously improve annotation quality. Required Skills & Qualifications Strong attention to detail. Strong analytical and problem-solving skills. Excellent pattern recognition and observation abilities. Ability to understand and consistently follow detailed annotation guidelines. Good written English communication skills. Basic computer proficiency and willingness to learn new annotation tools. Highly committed, dependable, and self-motivated. Ability to work independently with minimal supervision. Ability to manage time effectively and consistently meet deadlines. Available to work at least 40 hours per week. Technical Requirements Desktop computer or laptop with at least an Intel Core i5 (or AMD Ryzen 5 equivalent). Minimum 8 GB RAM (16 GB recommended). Stable internet connection with a minimum speed of 300 Mbps. Quiet and professional remote work environment. Backup internet or power source is an advantage but not required. Preferred Qualifications Previous experience in AI/ML data annotation. Experience using annotation tools such as CVAT, Labelbox, Label Studio, SuperAnnotate, or similar platforms. Experience with: Bounding Box Annotation Semantic & Instance Segmentation Polygon Annotation Object Tracking Keypoint Annotation LiDAR Annotation Video Annotation OCR/Text Annotation Familiarity with autonomous driving datasets is a plus. Experience working on large-scale annotation projects. Quality Expectations Successful annotators are expected to: Maintain consistently high annotation accuracy. Follow project guidelines without unnecessary deviations. Report uncertain or ambiguous cases rather than making assumptions. Accept and apply QA feedback constructively. Continuously improve annotation quality and efficiency. Produce work that minimizes QA revisions and rework. Performance Metrics Performance may be evaluated based on: Annotation Accuracy Consistency Productivity Guideline Compliance Turnaround Time Responsiveness to QA Feedback Overall Reliability Deliverables Accurately completed annotation tasks. Timely submission of assigned work. Proper documentation of edge cases or unclear scenarios. Consistent adherence to project guidelines and quality standards. Working Style We are looking for individuals who are: Detail-oriented Highly analytical Self-driven Reliable and accountable Open to feedback Fast learners Comfortable working independently Able to maintain both quality and productivity under deadlines Engagement Details Hourly or Fixed-Price (based on project and experience) Initial paid trial period Opportunity for long-term engagement Flexible working hours with consistent availability preferred Minimum commitment of 40 hours per week How to Apply Please include the following: A brief introduction about yourself. Your relevant annotation experience. Annotation tools you have used. Types of datasets you have worked on (Image, Video, LiDAR, Text, etc.). Your weekly availability. Your computer specifications. Your internet speed (include a screenshot of a speed test if available). A brief explanation of how you ensure quality and consistency in your annotation work. What We're Looking For We're seeking annotators who naturally notice details that others miss. The ideal candidate values accuracy over shortcuts, takes ownership of their work, communicates effectively, and is committed to producing high-quality training data that helps build better AI systems. If you enjoy solving visual puzzles, spotting inconsistencies, and continuously improving your work, we'd love to hear from you.