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Internship Remote Data Annotation Jobs in Kentucky

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Internship Remote Data Annotation information

What is the difference between Internship Remote Data Annotation vs Data Labeling Specialist?

AspectInternship Remote Data AnnotationData Labeling Specialist
CredentialsTypically students or entry-level with basic computer skillsRelevant experience or certifications in data annotation or related fields
Work EnvironmentRemote, flexible hours, often part-timeRemote or on-site, depending on employer, often full-time
Industry UsageCommon in AI/ML projects, tech companies, research institutionsUsed in AI/ML, autonomous vehicles, healthcare, and tech sectors

Internship Remote Data Annotation roles are usually entry-level, temporary positions aimed at gaining experience, while Data Labeling Specialists are more experienced roles focused on accurately annotating data for machine learning models. Both roles are essential in AI development but differ in experience requirements and job scope.

What typical tasks can I expect to handle as a remote data annotation intern, and how is performance usually evaluated?

As a remote data annotation intern, your primary tasks will involve reviewing and labeling data—such as images, text, or audio—according to specific guidelines provided by your team. You'll likely work with annotation tools, follow detailed instructions to ensure high-quality and consistent labeling, and may participate in quality assurance checks. Performance is generally evaluated based on annotation accuracy, speed, and your ability to follow instructions, with regular feedback provided via virtual meetings or project management platforms. Effective communication and attention to detail are key to succeeding in this collaborative, remote environment.

What are the key skills and qualifications needed to thrive as a Remote Data Annotation Intern, and why are they important?

To thrive as a Remote Data Annotation Intern, you need attention to detail, basic data processing skills, and familiarity with labeling guidelines, generally supported by a high school diploma or relevant coursework. Experience with annotation platforms, spreadsheets, and sometimes basic programming tools or machine learning frameworks is often required. Strong communication, time management, and the ability to follow precise instructions are valuable soft skills in this role. These skills ensure high-quality, accurate data labeling, which is critical for training reliable machine learning models.

What is a remote data annotation internship?

A remote data annotation internship is a temporary position where interns work from home or another remote location to label, categorize, or tag data such as images, text, or audio. This annotated data is often used to train machine learning models and improve artificial intelligence systems. Interns typically use specialized platforms or tools to complete their tasks, and gain hands-on experience in data handling, quality control, and understanding AI workflows. The internship is ideal for those interested in technology, data science, or AI, and often requires strong attention to detail and good communication skills.
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Internship Remote Data Annotation jobs near you
DevOps Engineer (Full Time - Remote)

DevOps Engineer (Full Time - Remote)

Braintrust

Alexandria, KY • Remote

$60 - $90/hr

Full-time

This job post has expired today. Applications are no longer accepted.

Job description

Company
Braintrust is a global talent network that connects top independent professionals with leading companies for high-quality, flexible work. We help organizations hire skilled talent faster while giving professionals access to vetted opportunities with innovative teams.Job description

This contract will begin with a 1 month paid trial which can then extend to 6+ months


Job description:
We're hiring experienced DevOps engineers to author and validate Infrastructure-as-Code (IaC) tasks. You'll design realistic infrastructure scenarios, build the ground-truth solutions, and define the automated checks that grade whether an AI agent solved them correctly and safely. This is hands-on engineering and judgment work — you're encoding what "good" looks like for real infrastructure operations into verifiable tasks. If you've spent years writing Terraform, Pulumi, CloudFormation, cloud CLI, for AWS or GCP, we want to work with you.
What you'll do:

  • Author IaC tasks grounded in real-world AWS and GCP) scenarios.
  • Build ground-truth solutions for each task: correct, idempotent IaC that converges to the desired end state.
  • Design verifiable graders — automated checks that confirm an agent reached the correct end state
  • Review and QA tasks authored by other engineers for correctness, difficulty calibration, and robustness.
  • Harden tasks against reward hacking
  • Document task intent, assumptions, edge cases, and scoring rationale clearly.


Must-have qualifications:

  • 4+ years in DevOps / cloud engineering.
  • Deep, hands-on Infrastructure-as-Code expertise: Terraform (required); Pulumi, CloudFormation, or CDK a plus.
  • Strong AWS and GCP depth
  • Comfortable scripting in Python (and/or Bash) to build and automate validation.
  • A clear sense of correctness and verification — you can articulate, in code, what makes an infrastructure outcome right and safe
  • Prior work in AI evaluation, benchmarking, or expert data annotation preferred.
  • Prior experience with LocalStack preferred

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