Job overview
Northlight Digital Services is hiring entry-level Data Annotation Specialists to label and categorize text, images, audio, and video that trains AI models. You will follow detailed annotation rubrics, review AI-generated content for accuracy and quality, flag errors, biases, and edge cases, meet daily annotation targets, report ambiguous cases to project leads, participate in quality calibration sessions, and maintain confidentiality of project data.
No prior data annotation or AI experience is required. Paid training is provided. No coding, design, or technical background is needed. Training covers annotation platforms (Labelbox, Scale AI, Appen, Surge, Remotasks), labeling guidelines, and quality standards. This role suits career changers, recent graduates, students, stay-at-home parents, and anyone interested in AI, language, or data.
This opportunity may suit people searching for data annotation specialist remote, data annotation jobs no experience, ai training data jobs work from home, or remote data labeling jobs entry level.
No AI Experience or Coding Required: Training Covers Everything
This is a true entry-level role. No prior data annotation, AI, or machine learning experience is required, and you do not need to know how to code. You will receive paid training on the annotation platforms used by the project (Labelbox, Scale AI, Appen, Surge, or Remotasks), the specific labeling guidelines, and quality standards. What matters most is that you can follow detailed written guidelines consistently, maintain focus on repetitive tasks, and know when to escalate ambiguous cases. If you have ever followed a recipe carefully or proofread a document line by line, you already have the fundamental skill.
Data Annotation Is Not the Same as Prompt Writing or Content Moderation
Data annotation sits at a specific point in the AI pipeline. You are not writing prompts (that is a prompt writer), not reviewing user content for policy violations (that is a content moderator), and not testing software (that is a QA tester). You are labeling and categorizing raw data — text, images, audio, video — so that AI models can learn from it. You review AI outputs for accuracy, flag errors and biases, and document edge cases. The work is structured, guideline-driven, and measured in daily task targets. Accuracy and consistency matter more than speed, though both are tracked.
Full-Time, Part-Time, and Contract Projects
Full-time roles typically run 40 hours per week. Part-time and contract roles are often project-based, running 2–12 weeks depending on the client and data volume. Some projects allow flexible hours; others require specific shift coverage for teams working across time zones. Contract roles are paid hourly based on project scope. Some projects pay per-task instead of hourly — discuss the payment structure during your interview. This is a field where project-based work is normal, and successful annotators often move between projects with different clients.
What you'll do
- Label and categorize data according to project guidelines
- Annotate text, images, audio, or video for AI training
- Review AI-generated content for accuracy and quality
- Flag errors, biases, or edge cases in AI outputs
- Follow detailed annotation rubrics and style guides
- Maintain high accuracy and consistency across tasks
- Meet daily annotation targets (often hundreds of items)
- Report ambiguous or unclear cases to project leads
- Participate in quality calibration sessions
- Document annotation decisions where required
- Maintain confidentiality of project data
- Work within annotation platforms (Labelbox, Scale AI, Appen, Surge, Remotasks)
- Support model evaluation and red-teaming where needed
- Provide feedback on annotation guidelines
Daily workflow
Depending on the project and annotation type, a typical day may look like this:
Required qualifications
- No prior data annotation or AI experience required; paid training provided
- No coding, design, or technical background required
- High school diploma or equivalent required
- Strong attention to detail and consistency
- Ability to follow detailed written guidelines
- Basic computer literacy and comfort with web apps
- Strong written English for notes and edge-case documentation
- Ability to maintain focus on repetitive tasks
- Comfort with ambiguity — knowing when to escalate
- Reliability with daily task targets
- Reliable internet connection and a quiet workspace
- Ability to work independently in a remote environment
Preferred qualifications
- Prior experience with data annotation or AI training data
- Familiarity with Labelbox, Scale AI, Appen, Surge, or Remotasks
- Interest in AI, machine learning, or language
- Strong reading comprehension and analytical skills
- Bilingual (Spanish/English) is a strong plus for multilingual annotation
- Experience with content moderation or quality assurance
- Comfort with repetitive, structured work
Benefits
Paid training
Learn annotation platforms, labeling guidelines, and quality standards on the clock.
Work from home
No commute and a flexible home setup.
Schedule options
Full-time, part-time, and contract project roles available.
Career growth
Paths to Senior Annotator, Quality Analyst, AI Trainer, Data Operations Lead, or AI Product Specialist.
Health benefits
Medical, dental, and vision coverage may be available for full-time roles (details confirmed at interview).
Clear pay
Hourly pay listed up front. Some projects pay per-task instead of hourly — discuss during interview.
Remote work equipment for this role
Most employers expect you to have the basics. Equipment support details are confirmed during your interview.
Computer
A reliable laptop or desktop — annotation platforms run in the browser but benefit from a larger screen.
Internet
A stable, high-speed connection. Annotation platforms require consistent bandwidth.
Headphones
Required for audio annotation projects. Useful for video annotation as well.
Workspace
A quiet, private spot for focused work on repetitive tasks.
Never pay to get hired
Real employers do not charge for training, equipment or background checks. Read our guide on how to spot remote job scams.
Application process
Apply Online
Send the short form and your resume. Share any detail-oriented work, language, or analytical experience.
Quick Screen
We check your setup and ask about your comfort with repetitive tasks, following guidelines, and attention to detail.
Employer Interview
Meet the project team, discuss the annotation type, and learn about quality expectations and daily targets.
Paid Training & Go-Live
Complete paid training on the annotation platform and guidelines, then start annotating.
Frequently asked questions
Do I need AI or machine learning experience?
No. This is a true entry-level role. Paid training covers the annotation platforms, labeling guidelines, quality standards, and the specific data types you will be working with. No prior AI or machine learning experience is required.
Do I need to know how to code?
No coding is required. You will use annotation platforms through their web interfaces — no programming, data science, or technical background is needed. However, you do need to be comfortable following detailed written guidelines and working within structured software tools.
What is data annotation, exactly?
Data annotation is the process of labeling and categorizing data — text, images, audio, or video — so that AI models can learn from it. For example, you might draw boxes around objects in images, tag the sentiment of customer reviews, transcribe audio, or categorize video clips. You follow a detailed rubric so that the labels are consistent across annotators. The quality of the training data directly affects how well the AI model performs.
Is this the same as AI prompt writing?
No. Prompt writing involves crafting instructions and questions for AI models to respond to. Data annotation involves labeling and categorizing the data that trains those models. They are different roles at different stages of the AI pipeline. This page is for data annotation.
Is this the same as content moderation?
No. Content moderation involves reviewing user-generated content for policy violations. Data annotation involves labeling data according to a rubric — which may include flagging problematic content, but the focus is on categorizing and structuring data for AI training, not on enforcing platform policy. The skill sets overlap but the jobs are distinct.
How many items will I annotate per day?
Daily targets vary widely by project and annotation type. Simple classification tasks may have targets in the hundreds or thousands. Complex tasks — like detailed video annotation or nuanced language tasks — may have much lower daily targets. The target is set so that a trained annotator can meet it while maintaining the required quality level. Discuss the specific target during your interview.
What platforms will I use?
Depending on the project, you may use Labelbox, Scale AI, Appen, Surge, or Remotasks. Training covers the specific platform used by the project. You will work through a web browser and do not need to install or configure software.
Is the work repetitive?
Yes, by nature. Annotation work involves following a detailed rubric on many similar items. The variety comes from the type of data — text, images, audio, video — and from the different projects you may work on over time. If you can maintain focus and consistency on structured, repetitive work, you will do well in this role.
Will I need to sign a confidentiality agreement?
Likely yes. Annotation work often involves proprietary or sensitive data — internal documents, unreleased product data, or customer content. Employers typically require a confidentiality or NDA agreement. Maintaining confidentiality of project data is a core requirement of the role.
What is the pay range?
The pay range for this role is $15–$22 per hour, based on project scope and complexity. Entry-level candidates typically start near the lower end. Some projects pay per-task instead of hourly. Discuss the payment structure during your interview.
Will I need to work weekends?
Some projects require weekend coverage for teams working across time zones or against project deadlines. Other projects allow flexible hours within a weekly hour target. Discuss schedule expectations during your interview.
Does it cost anything to apply?
No. Applying through JobSphereHR is always free. Beware of any "data annotation" role that asks for payment, software purchases, or credentials — those are scams. Read our guide on spotting remote job scams.
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