Roles & Responsibilities:
1. Data Annotation Specialist / Video Analyst
• Annotate images and videos using internal tools with high accuracy.
• Label keyframes, objects, actions, and events per guidelines.
• Follow complex instructions and maintain annotation quality standards.
• Work with large datasets and ensure consistency in annotations.
• Communicate issues or ambiguities in annotation instructions.
• Collaborate with QA teams to improve annotation processes.
2. Synthetic Data Verifier
• Review and verify synthetic data generated using 3D simulations or AI tools.
• Compare real-world and synthetic datasets for accuracy and realism.
• Report anomalies, bugs, or deviations in synthetic environments.
• Suggest improvements to the synthetic data generation process.
3. Annotation Tech Lead
• Lead a team of annotators and analysts across various projects.
• Ensure timely delivery and quality of annotated datasets.
• Design and maintain annotation workflows, SOPs, and quality checks.
• Provide technical guidance on tools, scripting (e.g., Python), and automation.
• Liaise with ML/AI engineers to understand data needs and iterate on labeling
strategies.
• Train and mentor new team members on best practices and tools.
Key Skills Required:
• Strong attention to detail and pattern recognition.
• Experience with data annotation tools (e.g., CVAT, Labelbox, V7, SuperAnnotate).
• Familiarity with video tagging, frame-by-frame analysis, or temporal annotation.
• Basic understanding of computer vision concepts is a plus.
• Experience with Python or automation tools is a bonus (for tech leads).
• Ability to work in a fast-paced and iterative environment.
Qualifications:
• Bachelor’s degree in any discipline (Engineering, Computer Science, or related field
preferred).
• 4-6 years of relevant experience (higher experience for tech lead roles).
• Prior experience in data labeling or AI/ML projects preferred.
Nice to Have:
• Exposure to synthetic data tools (e.g., Unreal Engine, Unity, Blender).
• Understanding of AI model training pipelines.
• Experience in QA or process improvement for annotation pipelines.