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Data Labelling Jobs in Indiana (NOW HIRING)

In this role, you will participate in tasks that help improve machine learning models, including data labeling, content evaluation, and user-based testing. Responsibilities : • Perform AI/ML ...

In this role, you will participate in tasks that help improve machine learning models, including data labeling, content evaluation, and user-based testing. Projects may vary in scope and format ...

In this role, you will participate in tasks that help improve machine learning models, including data labeling, content evaluation, and user-based testing. Projects may vary in scope and format ...

In this role, you will participate in tasks that help improve machine learning models, including data labeling, content evaluation, and user-based testing. Projects may vary in scope and format ...

In this role, you will participate in tasks that help improve machine learning models, including data labeling, content evaluation, and user-based testing. Projects may vary in scope and format ...

In this role, you will participate in tasks that help improve machine learning models, including data labeling, content evaluation, and user-based testing. Projects may vary in scope and format ...

In this role, you will participate in tasks that help improve machine learning models, including data labeling, content evaluation, and user-based testing. Projects may vary in scope and format ...

... a Data Scientist to provide engineering and technical support to assist our government customer ... Define metadata requirements and schemas to support the complex labeling of military motion imagery ...

Data Insights Manager Status: Full-time Hours: 40/week, Monday - Friday, 9 am-6 pm Location ... Create regular and ad-hoc reports to inform label partners, artists, and internal teams on key ...

Data Insights Manager Status: Full-time Hours: 40/week, Monday - Friday, 9 am-6 pm Location ... Create regular and ad-hoc reports to inform label partners, artists, and internal teams on key ...

Data Insights Manager Status: Full-time Hours: 40/week, Monday - Friday, 9 am-6 pm Location ... Create regular and ad-hoc reports to inform label partners, artists, and internal teams on key ...

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Perform cable management, labeling, and patching, ensuring all work adheres to established ... Collaborate with data center engineers, electricians, and other team members to coordinate ...

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Data Labelling information

See Indiana salary details

$43.8K

$157K

$231.7K

How much do data labelling jobs pay per year?

As of Aug 4, 2026, the average yearly pay for data labelling in Indiana is $157,025.00, according to ZipRecruiter salary data. Most workers in this role earn between $127,000.00 and $161,800.00 per year, depending on experience, location, and employer.

Is data labelling a good career?

Data labelling is a common entry-level role in data annotation and machine learning workflows, often requiring attention to detail and familiarity with labeling tools. It can provide opportunities to develop skills in data management and AI, but typically offers lower pay and limited advancement without additional training or experience.

What is a data labelling?

A Data Labelling job involves annotating data, such as text, images, audio, or video, to help train machine learning models. Labelers categorize or tag data by following specific guidelines to ensure accuracy and consistency. This process is essential for improving AI applications, including image recognition, natural language processing, and autonomous systems. Attention to detail and adherence to instructions are key skills required for this role.

How much do data labelers get paid?

Data labelers typically earn between $10 and $20 per hour, depending on experience, complexity of tasks, and the employer. Some positions may offer project-based pay or bonuses for accuracy and efficiency.

What are the typical daily responsibilities of a data labelling professional?

Data Labelling professionals are generally responsible for reviewing and accurately annotating large volumes of data—such as images, audio, video, or text—to support machine learning and AI projects. This often involves using specialized labeling platforms and following detailed guidelines provided by data scientists or project managers. You may also participate in regular team meetings to discuss quality standards or address ambiguities in data, and your work is typically reviewed for accuracy before being integrated into training datasets. Collaborating with other data annotators, engineers, and analysts is a common part of the process to ensure consistency and high-quality results.

What are the key skills and qualifications needed to thrive in the data labelling position, and why are they important?

To thrive as a Data Labelling professional, you need strong attention to detail, proficiency with data annotation processes, and a basic understanding of machine learning concepts. Familiarity with annotation tools like Labelbox, Supervisely, or Amazon SageMaker Ground Truth is often required, and some roles may value certifications in data processing or AI fundamentals. Reliability, patience, and the ability to follow precise instructions are important soft skills for success in this position. These skills ensure accurate and consistent data labeling, which is critical for developing effective AI models and maintaining data integrity.

What are data labeling jobs?

Data labeling jobs involve annotating or tagging data such as images, text, or videos to help machine learning models learn and improve. These roles typically require attention to detail and familiarity with labeling tools or software, and may be performed remotely or in a controlled environment.

How can I get started in data labeling?

To start in data labeling, gain familiarity with common tools like labeling software and understand data annotation standards. Building attention to detail and basic knowledge of the data types you will label, such as images or text, is essential. Many entry-level roles require no formal certification but may prefer candidates with basic computer skills and the ability to follow detailed instructions.
What are the most commonly searched types of Data Labelling jobs in Indiana? The most popular types of Data Labelling jobs in Indiana are:
What are popular job titles related to Data Labelling jobs in Indiana? For Data Labelling jobs in Indiana, the most frequently searched job titles are:
What job categories do people searching Data Labelling jobs in Indiana look for? The top searched job categories for Data Labelling jobs in Indiana are:
Infographic showing various Data Labelling job openings in Indiana as of July 2026, with employment types broken down into 1% As Needed, 82% Full Time, 13% Part Time, 1% Temporary, and 3% Contract. Highlights an 88% Physical, 3% Hybrid, and 9% Remote job distribution, with an average salary of $157,025 per year, or $75.5 per hour.

Audio Collection & Transcription - Native U.S. English Speakers

Chemin

Brazil, IN • Remote

Other

Posted 21 days ago


Job description

About Kaya

Kaya by Chemin Sdn Bhd is a community for high-performing data annotators who play an integral role in shaping the future of machine learning and artificial intelligence.

Kaya offers a collaborative environment where ambitious annotators can thrive. It is a tight-knit community that supports members' professional growth and helps them build a long-term career in data labelling and AI.

Join Kaya and start contributing to impactful AI projects.

About the Role

We are looking for native U.S. English speakers to join a short-term remote project helping train AI language systems.

Your task in this project is to record natural customer service conversations based on client-provided scripts and accurately transcribe your own recordings. These conversations reflect how people naturally speak in real-life customer service interactions, helping AI better understand authentic U.S. English speech.

If you're comfortable working with audio, detail-oriented, and can commit a few hours a day, this project is for you.

Requirements

What You'll Be Doing
  • Record natural customer service conversations by following client-provided scripts
  • Transcribe your own recordings accurately
  • Segment audio into natural speech units
  • Identify and label speakers when multiple voices are present
  • Apply transcription guidelines, punctuation, and annotation tags correctly
  • Follow project guidelines to maintain high-quality outputs
  • Communicate actively with the Project Manager for clarifications
Project Details
  • Duration: Approximately 2 months
  • Time commitment: Around 4-6 hours per day
  • Onboarding Training: Mandatory (TBD, during office hours 9:00 AM-6:00 PM GMT-3)
  • Working hours: Flexible (tasks can be completed anytime before the assigned deadline)
Pay Rate
  • Audio Collection: USD 1 per accepted audio minute
  • Transcription: USD 2 per accepted task (AHT: ~30-60 minutes per task)
  • Project Period: July - August 2026
What Do I Need?
  • Native speaker of U.S. English
  • Strong listening, reading, and writing skills in English
  • Excellent attention to detail and ability to follow written guidelines
  • Detail-oriented and patient, able to maintain at least 95% accuracy
  • Willing to ask questions when unsure
  • Quiet environment suitable for recording audio
  • Laptop or desktop with a stable internet connection
  • Ability to manage your own time and meet deadlines
  • Willing to complete a mandatory pre-assessment as part of the screening process, assessment here: https://forms.gle/Vkhfz2fGVXiWvHMv6

A mandatory pre-assessment will be shared to evaluate whether your skill and language proficiency meets the project's quality standards.

Bonus point: Previous experience in transcription, audio annotation, data labelling, or AI-related projects.

Benefits

    • Work remotely from anywhere
    • Be part of an AI project representing authentic U.S. English conversations
    • Priority consideration for future opportunities
    • Gain hands-on experience in AI language data, transcription, and audio annotation
    • Contribute to building smarter and more inclusive AI language technologies