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Annotation Labelling Jobs in Kentucky (NOW HIRING)

... labeling, or related fields. * Lead the day-to-day operation of the AI annotation program. * Manage a team of annotators responsible for reviewing customer conversations and AI interactions.

$120K - $155K/yr

You will help build the data engine behind Simbe's AI platform: model assisted labeling, data quality checks, annotation guidelines, error mining, dataset versioning, active learning, and evaluation ...

Our Data Annotation team turns raw teleoperation footage into the labels our imitation learning models depend on, so throughput and quality here are a direct input to how fast the company ships. Dyna ...

As a Data Annotation Specialist at Dyna Robotics, you will be pivotal in iterating on our AI system ... Manually annotate video sequences (boxes/masks/keypoints), track IDs, and label actions & temporal ...

As we open our new Salt Lake City office and grow our annotation team, we are looking for a Video ... Experience with data labeling, AI/ML operations, video review, regulated workflows, or public ...

$150K - $190K/yr

Train and integrate lightweight, custom CV models, active learning workflows, and pre-labeling agents specifically designed to accelerate human annotation and data quality control. * Data Curation ...

Develop and maintain annotation standards, labeling guidelines, and data governance processes to ensure consistency and reproducibility across projects * Conduct IV&V of datasets and model outputs to ...

The Team Lead will operate annotation as a shared service across our Data AI teams. They will ensure that centrally defined standards: schemas, labels, ambiguity frameworks, and calibration rules ...

$90K - $125K/yr

Direct experience managing a labeling, annotation, or data-quality team * Extremely detail-oriented -- you notice when data is wrong, inconsistent, or incomplete before anyone points it out * Strong ...

Perform AI/ML-related tasks such as data labeling, annotation, and content evaluation * Participate in remote assignments or attend on-site sessions when required * Follow project guidelines and ...

Perform AI/ML-related tasks such as data labeling, annotation, and content evaluation * Participate in remote assignments or attend on-site sessions when required * Follow project guidelines and ...

Perform AI/ML-related tasks such as data labeling, annotation, and content evaluation * Participate in remote assignments or attend on-site sessions when required * Follow project guidelines and ...

Perform AI/ML-related tasks such as data labeling, annotation, and content evaluation * Participate in remote assignments or attend on-site sessions when required * Follow project guidelines and ...

Perform AI/ML-related tasks such as data labeling, annotation, and content evaluation * Participate in remote assignments or attend on-site sessions when required * Follow project guidelines and ...

Perform AI/ML-related tasks such as data labeling, annotation, and content evaluation * Participate in remote assignments or attend on-site sessions when required * Follow project guidelines and ...

$140K - $180K/yr

The harder half is knowing whether the labels are any good in the first place. Our annotation pipeline is built to measure itself: cases are claimed without race conditions, annotators move through ...

Bonus: familiarity with data annotation, labeling, or data operations workflows * Bonus: experience designing or scaling a hiring/screening pipeline Why Encord * Competitive salary, commission, and ...

Apply ML to labeling itself** Collaborate with ML engineers to design and integrate ML‐driven data annotation (pre‐labeling, autolabeling, active learning loops), helping us move from ...

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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.

What are popular job titles related to Annotation Labelling jobs in Kentucky?

For Annotation Labelling jobs in Kentucky, the most frequently searched job titles are:

What job categories do people searching Annotation Labelling jobs in Kentucky look for?

The top searched job categories for Annotation Labelling jobs in Kentucky are:

Data Annotation Engineer

Louisville, KY • On-site

Other

Posted 4 days ago


Job description

JD:

We are looking for an Annotation Program Lead to manage our AI Quality Annotation Program. This individual will oversee the operational execution of human review activities that establish the gold-standard datasets used to measure conversational AI performance, safety, and compliance.

The ideal candidate combines strong program management skills with experience in quality assurance, data annotation operations, and stakeholder engagement.

  • 5+ years of experience in program management, operations, quality assurance, data labeling, or related fields.
  • Lead the day-to-day operation of the AI annotation program.
  • Manage a team of annotators responsible for reviewing customer conversations and AI interactions.
  • Develop annotation guidelines, procedures, and quality standards.
  • Establish calibration programs and quality assurance processes to ensure consistency across reviewers.
  • Partner with Data Scientists to create and maintain gold-standard datasets.
  • Monitor annotation accuracy, throughput, and quality metrics.
  • Experience leading teams and managing operational workflows.
  • Strong organizational and stakeholder management skills.
  • Ability to analyze quality metrics and drive continuous improvement initiatives.
  • Excellent written and verbal communication skills.
  • Familiarity with Python/R, SQL and Excel. Should be familiar with running scripts on language data.