Data Annotation Specialist

About the Role

This position is for a detail-driven remote Data Annotation Specialist to support an expanding AI and machine learning function. The work centers on creating accurate, reliable datasets that help train and improve modern AI systems.

You will label, classify, review, and verify data across multiple formats, including text, images, audio, video, and structured records. The datasets you handle may be used for computer vision, natural language processing, speech recognition, and generative AI initiatives.

The role involves close coordination with machine learning engineers, AI researchers, project managers, and quality assurance colleagues to keep datasets consistent, accurate, and ready for production use. Success in this role depends on strong attention to detail, analytical thinking, and the ability to work efficiently in a fast-moving remote setting.

Training will be provided on annotation tools, project-specific instructions, and evolving AI workflows. Curiosity, precision, continuous improvement, and teamwork are highly valued.

Key Responsibilities

• Apply labels, categories, and classifications to large datasets according to project rules and annotation standards.
• Handle diverse data formats such as text, images, audio clips, video files, and structured data.
• Carry out computer vision tasks including object detection, segmentation, bounding boxes, semantic labeling, and keypoint marking when needed.
• Annotate language datasets for sentiment, intent, entity extraction, moderation, and document categorization tasks.
• Work on speech and audio data by checking transcriptions, identifying speakers, aligning timestamps, and validating pronunciation where required.
• Keep annotations consistent and aligned with documentation and quality expectations.
• Hit daily and weekly output targets while preserving a high standard of accuracy.
• Inspect completed datasets for missing labels, mistakes, and inconsistencies.
• Run quality checks before submitting annotation work.
• Review the work of other annotators to confirm accuracy and consistency.
• Escalate unclear or ambiguous samples to project leads for guidance.
• Follow quality assurance processes and look for ways to improve annotation precision.
• Partner with machine learning engineers to understand dataset needs and model goals.
• Work with AI researchers to refine labeling rules and improve consistency.
• Keep project managers updated on progress, blockers, and delivery timelines.
• Join team meetings, calibration sessions, and review discussions as needed.
• Share feedback on workflows, documentation, and tool enhancements.
• Support cross-functional teams during project launches and dataset setup.
• Manage assigned tasks through annotation platforms and project tracking tools.
• Protect confidential information and follow company data-handling policies.
• Maintain records related to completed tasks, quality indicators, and productivity.
• Report issues with tools or datasets without delay.
• Follow version control and documentation practices for annotation work.
• Assist with dataset organization and metadata checks when needed.
• Spot recurring challenges in the workflow and suggest improvements.
• Help refine annotation guidelines to support greater consistency.
• Take part in pilot projects and provide feedback on usability.
• Contribute to better efficiency without reducing quality.
• Support internal best practices and documentation development.
• Keep up with new developments in AI, machine learning, and annotation technology.
• Learn new tools, methods, and quality assurance practices.
• Participate in training and knowledge-sharing sessions.
• Continuously improve speed, accuracy, and productivity.
• Adapt quickly as project requirements and guidelines change.

Requirements

• 1 to 2 years of professional experience in data annotation, data labeling, quality assurance, content moderation, data processing, or a similar practical role.
• Strong focus on detail and a consistent commitment to high-quality output.
• Ability to follow detailed instructions and annotation rules accurately.
• Experience with annotation work involving text, images, audio, video, or structured data.
• Basic understanding of artificial intelligence and machine learning concepts.
• Strong analytical thinking and problem-solving ability.
• Good organization and time-management skills.
• Comfort working independently in a remote setup.
• Clear written and spoken communication skills.
• Working knowledge of Microsoft Office or Google Workspace tools.
• Ability to meet productivity goals while keeping accuracy high.
• Responsible handling of confidential and sensitive information.

Preferred Experience

• Experience with annotation platforms such as Labelbox, Label Studio, CVAT, SuperAnnotate, Scale AI, or similar tools.
• Familiarity with computer vision labeling methods such as bounding boxes, polygons, segmentation masks, and keypoint tagging.
• Exposure to natural language processing dataset annotation.
• Understanding of speech and audio transcription processes.
• Awareness of large language models and generative AI.
• Basic knowledge of Python or SQL for data handling.
• Background in quality assurance or data validation work.
• Familiarity with Agile or Scrum delivery methods.
• Experience on multilingual annotation projects.
• Understanding of data privacy rules and security practices.
• Experience with cloud-based annotation tools.
• A bachelor's degree in Computer Science, Data Science, Linguistics, Information Technology, or a related field is preferred but not mandatory.

What You'll Gain

• Practical exposure to advanced AI and machine learning projects.
• Hands-on experience across computer vision, NLP, speech AI, and generative AI use cases.
• The chance to work with AI researchers, data scientists, and machine learning engineers.
• Training on modern annotation tools and established best practices.
• A collaborative and supportive remote work environment.
• Ongoing learning and professional development opportunities.
• Experience contributing to datasets used in live production systems.
• Growth opportunities in AI operations, data quality, and machine learning support roles.
• The opportunity to influence the performance and reliability of AI products used by large user bases.

Ideal Candidate Profile

The right candidate is organized, dependable, analytical, and highly accurate. You should enjoy working with data, following detailed instructions, and supporting high-quality AI systems. Comfort with evolving project needs, remote collaboration, and continuous learning is important.