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Full Time Ai Data Annotation Jobs in Indiana (NOW HIRING)

TSMG

AI/ML Data Contributor

Indianapolis, IN

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 ...

TSMG

AI/ML Data Contributor

Indianapolis, IN

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 ...

TSMG

AI/ML Data Contributor

Indianapolis, IN · On-site

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 ...

The businesses of Merck KGaA, Darmstadt, Germany

Head of Materials AI EMD Electronics is in the middle of a fundamental shift in how R&D gets done ... You will influence how R&D scientists design experiments, capture data, and analyze results to ...

Onebridge

Snowflake Data Engineer

Indianapolis, IN · On-site

$109K - $131K/yr

Onebridge, a Marlabs Company, is a global AI and Data Analytics Consulting Firm that empowers ... A Best Place to Work in Indiana since 2015 Employment Type: FULL_TIME

Aircall

Product Designer - AI Agents

Francisco, IN

$125K - $160K/yr

How we work at Aircall: We're customer-obsessed, data-driven, and focused on delivering meaningful ... of full-time experience in-house at a company building product * Professional proficiency in ...

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Full Time Ai Data Annotation information

What are some common challenges faced by AI Data Annotation professionals, and how can they be addressed?

AI Data Annotation professionals often encounter challenges such as maintaining high accuracy while working with large datasets, interpreting ambiguous data, and consistently following complex labeling guidelines. These challenges can be addressed through thorough training, frequent communication with project managers or data scientists, and utilizing annotation tools with built-in quality checks. Collaboration with team members and regular feedback sessions also help ensure consistency and improve overall data quality, making the annotation process smoother and more efficient.

Which 3 jobs will survive AI?

Full Time AI Data Annotation jobs are likely to persist because they require human judgment for complex data labeling and quality control. Roles involving creative thinking, strategic decision-making, and interpersonal skills, such as AI trainers, data scientists, and AI ethicists, are also expected to remain in demand as AI systems need human oversight and expertise. These jobs often require specialized skills, certifications, or domain knowledge that are difficult for AI to fully replicate.

What are the key skills and qualifications needed to thrive as a Full Time AI Data Annotation Specialist, and why are they important?

To thrive as a Full Time AI Data Annotation Specialist, you need strong attention to detail, basic data literacy, and often a high school diploma or equivalent. Familiarity with annotation platforms (like Labelbox or Supervisely) and understanding of data labeling guidelines are typically required. Patience, consistency, and effective communication are soft skills that help ensure accuracy and clarity in collaborative projects. These skills and qualities are crucial for producing high-quality labeled data, which directly impacts the performance of AI models.

What are Full Time AI Data Annotation jobs?

Full Time AI Data Annotation jobs involve labeling, tagging, or categorizing data such as images, text, audio, or video to help train machine learning models. Annotators play a crucial role in ensuring AI systems understand and process information accurately by providing high-quality, human-curated data. These positions usually require attention to detail, basic computer skills, and the ability to follow specific guidelines for different projects. Full-time roles typically offer stable hours and may be remote or on-site, depending on the employer.

How to become an AI data annotator?

To become an AI data annotator, you typically need strong attention to detail, good reading comprehension, and basic computer skills. Familiarity with annotation tools and understanding of data labeling guidelines are also important; some roles may require prior experience or training. Many positions are entry-level and offer flexible schedules, making them accessible to a wide range of applicants.

What is a $900,000 AI job?

A $900,000 AI job typically refers to high-level roles in artificial intelligence, such as AI research directors, machine learning executives, or senior data scientists, often requiring advanced skills, extensive experience, and sometimes equity or performance bonuses. These positions are usually found in large tech companies or AI-focused firms and involve leadership, strategic planning, and cutting-edge development. Compensation at this level reflects significant responsibility and expertise in the field.

How much do AI data annotators make?

AI data annotators typically earn between $12 and $20 per hour, depending on experience, location, and the complexity of the annotation tasks. Many positions are remote and may offer flexible schedules, with some roles paying per project or data set rather than hourly.

What is the difference between Full Time Ai Data Annotation vs Data Labeler?

AspectFull Time Ai Data AnnotationData Labeler
CredentialsHigh school diploma or equivalent; some roles prefer basic technical skillsHigh school diploma or equivalent; minimal technical requirements
Work EnvironmentOffice or remote; part of AI development teamsOffice or remote; often task-based or freelance
Industry UsageUsed across AI, machine learning, and data science industriesPrimarily in AI and machine learning industries for data preparation
Job ScopeFull-time, with responsibilities including data annotation, quality control, and collaborationTask-specific, focusing on labeling data accurately for AI training

Full Time Ai Data Annotation roles typically require more consistent hours, team collaboration, and a broader scope of responsibilities compared to Data Labelers, who often work on individual tasks with minimal oversight. Both roles are essential in AI development, but Full Time Ai Data Annotation offers more stability and integration within AI projects.

What are the most commonly searched types of Ai Data Annotation jobs in Indiana? The most popular types of Ai Data Annotation jobs in Indiana are:
What are popular job titles related to Full Time Ai Data Annotation jobs in Indiana? For Full Time Ai Data Annotation jobs in Indiana, the most frequently searched job titles are:
What job categories do people searching Full Time Ai Data Annotation jobs in Indiana look for? The top searched job categories for Full Time Ai Data Annotation jobs in Indiana are:
What cities in Indiana are hiring for Full Time Ai Data Annotation jobs? Cities in Indiana with the most Full Time Ai Data Annotation job openings:
Full Time Ai Data Annotation jobs near you

AI/ML Data Contributor

TSMG

Indianapolis, IN

Full-time

Posted 2 days ago

Job description

Project Overview
We are currently hiring AI/ML Data Contributors to support a range of active and upcoming projects across the United States. 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, offering both remote and in-person opportunities (such as device or VR testing). This is a flexible, task-based role with the opportunity to participate in multiple projects over time.

Responsibilities
  • 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 ensure high-quality task completion
  • Provide feedback and input during testing activities
  • Complete tasks within given timelines
Requirements
  • Must be based in the United States
  • Strong attention to detail and ability to follow instructions
  • Basic computer skills and familiarity with digital tools
  • Reliable internet connection and access to a computer or smartphone
  • Availability to participate in task-based work (schedule may vary)
Nice to Have
  • Previous experience in data annotation, QA, or testing
  • Interest in AI, machine learning, or emerging technologies
What We Offer
  • Paid, flexible task-based work
  • Opportunity to work on innovative AI/ML projects
  • Exposure to cutting-edge technologies (including device and VR testing)
  • Potential for ongoing project participation
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
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