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Annotation Labelling Jobs (NOW HIRING)

The Labeling team delivers algorithms, tools and infrastructure to provide data labels that can be ... Perform high-precision mapping and spatial data annotation activities crucial for autonomous ...

Track data collection and annotation budget. 3. Annotation & Labeling Oversight * Coordinate data annotation activities with internal teams and external vendors. * Track annotation progress ...

Starfleet - Our Human Data Collection Platform (large-scale annotation, labeling, quality control, and human feedback systems) * Toolbox - Our unified Research Platform for model training ...

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Annotation Labelling information

What are the key skills and qualifications needed to thrive as an Annotation Labelling Specialist, and why are they important?

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 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 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.

More about Annotation Labelling jobs
What cities are hiring for Annotation Labelling jobs? Cities with the most Annotation Labelling job openings:
What states have the most Annotation Labelling jobs? States with the most job openings for Annotation Labelling jobs include:
What job categories do people searching Annotation Labelling jobs look for? The top searched job categories for Annotation Labelling jobs are:
Infographic showing various Annotation Labelling job openings in the United States as of May 2026, with employment types broken down into 7% Full Time, 92% Part Time, and 1% Contract. Highlights an 11% Physical, and 89% Remote job distribution.

Contract Labeling Associate

Stack AV

Pittsburgh, PA • On-site

Other

Posted 26 days ago


Job description

About the Role:

The Labeling team delivers algorithms, tools and infrastructure to provide data labels that can be used by Perception, Motion Planning, and other ML teams for training and evaluation. They work closely with manual labeling efforts and infrastructure teams to create a data centric ecosystem needed to develop real time, safety critical ML models for autonomous driving. 

We are looking for a Labeling Analyst to review and ensure a high quality of labels delivered to our Autonomy teams. The job entails operating internal and third party tools to visualize labels and checking if they meet our quality and labeling standards. The Labeling Analyst will also collaborate across operational and technical teams to identify and correct issues and support other labeling related tasks as needed.

Responsibilities:

  • Review labels using internal and third party tooling to identify defects, mis-labeled or unlabeled items.
  • Perform high-precision mapping and spatial data annotation activities crucial for autonomous vehicle navigation, utilizing proprietary tooling.
  • Update issue trackers based on defects found, summarize findings and write reports.
  • Follow up with internal and third party partners to resolve quality issues.
  • Participate in defining requirements for, testing, and creating user documentation for internal label editing and review tooling.
  • Help with other tasks such as selecting suitable logs for labeling and generating analytics on the QA process.
  • Partner with engineering teams to define requirements for, test, and create user documentation for new proprietary labeling and mapping tools.

Qualifications: 

  • Computer literacy and operate web based tools.
  • Ability to work with Google docs, spreadsheets, write summary reports and aggregate statistics using Google Sheets.
  • Ability to take high level acceptance criteria guidance and follow it to execute on label review.
  • Ability to work in a highly collaborative, team environment across a variety of functional domains, both operational and technical.