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Annotation Labelling information

What is annotation labelling?

Annotation labelling is the process of tagging or marking data—such as images, text, or audio—with relevant information or labels. This is an essential step in preparing datasets for machine learning and artificial intelligence models, as it helps algorithms understand and learn from raw data. Annotation labelling can include tasks like identifying objects in photos, transcribing speech, or categorizing text. Skilled annotators ensure accuracy and consistency to improve model performance. People in this role often use specialized tools or software to streamline and standardize the annotation process.

What are the key skills and qualifications needed to thrive as an annotation labelling specialist?

To thrive as an Annotation Labelling Specialist, you need strong attention to detail, data analysis capabilities, and familiarity with data annotation standards, usually supported by a background in computer science or related fields. Proficiency with annotation tools such as Labelbox, CVAT, or Supervisely, and sometimes knowledge of basic programming or scripting, is typically required. Excellent communication, consistency, and the ability to follow complex instructions are crucial soft skills for producing high-quality labeled data. These skills ensure the accuracy and reliability of datasets, which are foundational for successful machine learning and AI model development.

What are some common challenges faced by annotation labelling professionals, and how can they be managed?

Annotation Labelling professionals often encounter challenges such as maintaining high accuracy while handling repetitive data, meeting tight deadlines, and adapting to evolving project guidelines. To manage these, it’s important to develop strong attention to detail, regularly communicate with team leads to clarify instructions, and leverage annotation tools efficiently. Collaborating closely with quality assurance teams can also help identify and correct errors early, ensuring consistently high-quality outputs.

What is the difference between Annotation Labelling vs Data Labeling Specialist?

AspectAnnotation LabellingData Labeling Specialist
CredentialsBasic technical skills, attention to detailSimilar skills, sometimes additional domain knowledge
Work EnvironmentData annotation platforms, remote or officeData annotation tasks, often remote or in-office
Industry UsageAI, machine learning, autonomous vehiclesAI, machine learning, healthcare, retail
Search & ComparisonCommonly compared for entry-level data tasksRelated but broader role

Annotation Labelling involves marking data such as images, text, or videos to train AI models. Data Labeling Specialists perform similar tasks but may have a broader scope, including verifying and managing labeled data. Both roles are essential in AI development, often overlapping in skills and work environment, but Annotation Labelling is more focused on the annotation process itself.

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Annotation Labelling jobs near you
Infographic showing various Annotation Labelling job openings in the United States as of July 2026, with employment types broken down into 1% As Needed, 44% Full Time, 53% Part Time, and 2% Contract. Highlights an 46% Physical, 1% Hybrid, and 53% Remote job distribution.

Technical Product Manager - Data Annotation & Labelling

Skild AI

San Mateo, CA

$190K - $219K/yr

Other

Re-posted 17 days ago

Job description

Position Overview

We are looking for a Technical Product Manager - Data Annotation & Labelling with 5+ years of experience to lead and scale the full operations lifecycle for robotics data collection. This individual will manage a cross-functional team, build scalable systems, and make a significant impact in a rapidly evolving space. This role is crucial for driving execution and continuously improving workflows and systems to support rapid growth. This is a high visibility role that will have enormous impact on the company's trajectory. 

Responsibilities
  • Own and scale the full lifecycle for products pertaining to robotics data collection, labelling and annotation from physical setups to contractor management and annotation pipelines.
  • Drive data operations programs collaborating with operations managers, technicians and engineering
  • Build 0-1 solutions for large scale data pipelines
  • Work with executive leadership to develop data operations strategy and align these to overall corporate goal
Preferred Qualifications
  • 5+ years of experience in a fast-paced, startup-like environment
  • 2+ years in a technical role (e.g., engineer, program manager, product manager) at a technology company
  • Strong technical problem-solving skills, with the ability to quickly learn complex systems
  • Proven track record of supporting cross-functional stakeholders across customers, product, and engineering
  • Proven ability to communicate effectively with senior management
  • Ability to define and drive technology strategy
  • Previous entrepreneurial experience
  • Experience building products or initiatives from 0 to 1
  • BS/MS in Technical discipline

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