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If you’ve ever Googled “Data Annotator job description”, you’ve probably seen dozens of articles that all look the same: dry bullet points, vague wording, and no real insight into what the role actually means or how to attract the right candidates.
But here’s the problem with those cookie-cutter posts:
They don’t inspire anyone to apply—especially not top candidates who care about mission, culture, and growth. They simply check boxes like “Responsibilities, Duties, Requirements” and call it a day.
The truth is, a Data Annotator isn’t just ticking off tasks. They’re the people behind the scenes making AI work. Every label they add, every dataset they clean, directly impacts the accuracy of the technology your business relies on. In other words: the job matters, and your job description should reflect that.
Before we dive into examples, I recommend reading our full guide on how to write a job post that attracts top talent , Link https://workscreen.io/how-to-write-a-job-post/ if you haven’t already. It shows why generic job posts fail to attract quality candidates—and how to write one that actually connects.
In this guide, we’ll take what you learned there and apply it specifically to the Data Annotator role.
Don’t let bad hires slow you down. WorkScreen helps you identify the right people—fast, easy, and stress-free.
What A Data Annotator Actually Does - Their Roles
A Data Annotator is someone who prepares and labels data—text, images, audio, or video—so that machine learning models can understand and learn from it.
Think of them as the unsung heroes of AI. Without accurate annotations, even the most advanced algorithms fail.
In plain English:
- They look at raw data (like a picture of a dog, a snippet of audio, or a line of text).
- They tag, categorize, or highlight it according to strict guidelines.
- Their work becomes the foundation that trains AI systems to recognize patterns, make predictions, and improve accuracy.
But here’s the key: a great Data Annotator isn’t just fast. They’re detail-oriented, consistent, and able to follow complex instructions. Skills like patience, focus, and quality control matter just as much as technical tools.
And because they’re often working on sensitive or large-scale projects, traits like confidentiality, responsibility, and reliability are equally important.
Put simply: Data Annotators turn messy information into structured knowledge—and that’s what powers AI.
Two Great Data Annotator Job Description Templates
✅ Option 1:Job Description For Experienced Data Annotator
Job Title: Data Annotator – Computer Vision (Retail & Logistics)
Company: Arcadia Vision
Location: Remote (Americas & EMEA)
Type: Full-Time | Remote
Salary Range: $50,000–$65,000/year (depending on location & experience)
🎥 Meet the Hiring Manager
Watch a 60-second intro from Sam, Head of Data Operations: [Loom/YouTube link]
About Arcadia Vision
Arcadia Vision builds computer vision systems that help retailers and logistics companies track inventory, reduce waste, and improve on-shelf availability. Our models detect products, read labels, and flag damage across millions of images and video frames every week. We’re a remote-first team that values precision, calm focus, and continuous improvement—because great AI starts with great data.
What You’ll Do
- Label, classify, and segment images/video frames according to strict guidelines (e.g., object detection, instance segmentation, OCR).
- Perform QA passes to ensure annotation accuracy and consistency across projects.
- Partner with project managers and ML engineers to refine ontologies and edge-case handling.
- Flag ambiguous samples, propose clarifications, and contribute to playbook updates.
- Work in tools like Labelbox, CVAT, and internal annotation platforms.
What We’re Looking For
- 1–2+ years in data annotation or quality review for AI/ML projects.
- Proven accuracy under production timelines; strong attention to detail.
- Comfort with annotation tooling, spreadsheets, and ticketing systems.
- Clear written communication and the ability to follow complex specs.
Nice to Have
- Experience with retail/catalog data, OCR, or video pipelines.
- Background in linguistics, cognitive science, design QA, or similar.
Perks & Benefits
- Competitive salary + performance bonus
- Health, dental, and vision (or monthly health stipend for intl. teammates)
- 20 days PTO + local public holidays + paid sick days
- Monthly home-office/internet stipend + equipment provided
- Learning & development budget ($1,000/year)
- Flexible, async-friendly work with a calm, focus-first culture
Why This Role Is a Great Fit
- Direct model impact: Your labels feed production models used by real customers.
- Clear growth paths: Advance to Senior Annotator, QA Lead, or Ontology Specialist.
- Craft + ownership: Help shape playbooks, edge-case policies, and tooling UX.
- Supportive team: We measure twice, cut once—quality over chaos.
How We Hire
We review every application and keep you updated throughout. Shortlisted candidates complete a brief, fair skills evaluation via WorkScreen.io, followed by a structured interview. No take-home marathons, and we never ask for payment or sensitive financial info.
✅ Option 2: Job Description For Entry-Level Data Annotator (Training Provided)
Job Title: Junior Data Annotator – Training Provided
Company: Arcadia Vision
Location: Remote (Global)
Type: Full-Time | Remote
Salary Range: $18–$24/hour (or local-market equivalent)
🎥 Meet the Hiring Manager
Say hello in under a minute—Sam, Head of Data Operations: [Loom/YouTube link]
About Arcadia Vision
Arcadia Vision powers computer vision for retail and logistics—think product recognition, shelf monitoring, and damage detection at scale. Our annotators transform messy images and video into structured training data that makes our AI accurate, fair, and reliable. If you’re detail-oriented and curious about AI, we’ll teach you the rest.
What You’ll Do
- Tag and organize images/video using step-by-step playbooks.
- Learn industry-standard tools (Labelbox, CVAT) with mentoring and guided practice.
- Hit accuracy and throughput targets while maintaining consistency.
- Flag unclear cases and suggest improvements to instructions.
What We’re Looking For
- Comfort using computers; quick to learn new software.
- Patience, focus, reliability, and pride in doing careful work.
- Clear written communication and willingness to follow detailed instructions.
Nice to Have
- Experience in admin, QA, cataloging, or data entry.
- Familiarity with spreadsheets or ticketing tools.
Perks & Benefits
- Paid training & onboarding with a dedicated mentor
- Equipment + home-office/internet stipend
- Paid time off + local holidays + sick days
- Health stipend (region-dependent)
- Learning budget for courses/certifications
- Clear promotion paths to QA and Senior roles
Why This Role Is a Great Fit
- No prior experience required: We’ll train you on tools and quality standards.
- Real impact: Your work directly improves AI models used by global customers.
- Supportive runway: We move at a calm pace focused on accuracy and growth.
- Future-proof skills: Build a foundation in AI data operations.
How We Hire
Every application is reviewed. Candidates complete a short, beginner-friendly evaluation via WorkScreen.io to assess attention to detail and instruction-following, then a structured interview. We provide timely updates and never request fees or financial details.
If your hiring process is stressful, slow, or filled with second-guessing—WorkScreen fixes that. Workscreen helps you quickly identify top talent fast, eliminate low-quality applicants, and make better hires without the headaches.
Breakdown of Why These Data Annotator Job Posts Work
Now that you’ve seen two variations of a Data Annotator job description (experienced hire vs. entry-level), let’s unpack why they’re effective compared to the generic posts you’ll often find online.
1. Clear, Specific Job Titles
Instead of just saying “Data Annotator”, these posts add meaningful context:
- “Data Annotator – Computer Vision (Retail & Logistics)” instantly tells the candidate the domain they’ll be working in.
- “Junior Data Annotator – Training Provided” signals openness to beginners, which encourages applications from potential-driven candidates.
This level of specificity increases relevance and immediately filters for the right audience.
2. Video From the Hiring Manager
Both posts include a short Loom/YouTube video from the Head of Data Ops.
- This makes the company feel human and approachable.
- It creates instant trust by letting candidates see who they’d work with.
- In a sea of faceless, text-only job posts, this alone helps you stand out.
3. Company-Specific “About Us” Section
Instead of generic boilerplate, the “About Arcadia Vision” section explains:
- What the company does (computer vision for retail & logistics).
- Why the role matters (annotators ensure AI accuracy at scale).
- The company’s values (precision, calm focus, continuous improvement).
This shows mission, purpose, and personality—things top talent cares about far more than a list of duties.
4. Responsibilities That Show Impact
Tasks aren’t listed in vague terms like “label data.” Instead, they highlight:
- “Label, classify, and segment images/video frames according to strict guidelines.”
- “Your labels feed production models used by real customers.”
This framing helps candidates see how their work contributes to something bigger, which makes the job feel meaningful instead of mechanical.
5. Transparent Salary Ranges
Both posts clearly include compensation upfront. This:
- Builds trust and signals professionalism.
- Saves time for both the employer and the applicant.
- Attracts serious, qualified candidates while filtering out misaligned expectations.
6. Separate Sections for Perks & Benefits vs. Why the Role Fits
Instead of merging everything into one catch-all paragraph, the posts break it down:
- Perks & Benefits → tangible rewards like salary, PTO, stipends, equipment, training budget.
- Why This Role Is a Great Fit → emotional motivators like growth opportunities, meaningful impact, and supportive culture.
This dual approach appeals to both the logical and emotional drivers of candidate decision-making.
7. Respectful Hiring Process
Instead of a cold “only shortlisted candidates will be contacted,” the process is:
- Transparent (explains exactly what steps will happen).
- Respectful (every application is reviewed).
- Modern (uses WorkScreen.io for skill-based evaluation, not keyword-matching).
This builds trust and reduces the anxiety many candidates feel when applying.
8. Tone That Connects
Notice the tone:
- Warm, conversational, and human.
- Directly speaks to the candidate (“Your work directly improves AI models…”).
- Avoids corporate jargon.
The result? It feels like an invitation, not an instruction manual.
👉 Together, these elements make the job posts educational, inspiring, and practical—which attracts thoughtful, detail-oriented candidates (exactly what you want in a Data Annotator).
Example of a Bad Data Annotator Job Description (And Why It Fails)
❌ Bad Job Post Example
Job Title: Data Annotator
Company: Confidential
Location: Remote
Type: Full-Time
Job Summary
We are seeking a Data Annotator to support our AI projects. The candidate will be responsible for labeling data, reviewing tasks, and following instructions.
Key Responsibilities
- Label data as instructed.
- Complete tasks within deadlines.
- Report issues to management.
Requirements
- Bachelor’s degree preferred.
- Ability to work independently.
- Good attention to detail.
Compensation
Not disclosed.
How to Apply
Send your CV and cover letter to hr@company.com. Only shortlisted candidates will be contacted.
❌ Why This Job Post Fails
- Generic Job Title
Just says “Data Annotator” with no domain, focus area, or clarity on seniority. It doesn’t tell candidates what they’ll actually be working on or who it’s for. - Cold Company Presentation
Using “Confidential” signals nothing about mission, culture, or values. To top talent, it feels untrustworthy and uninspiring. - Empty Responsibilities
“Label data as instructed” could mean anything. There’s no sense of the scale, purpose, or impact. A top candidate looking for meaningful work will scroll right past. - Vague Requirements
A bachelor’s degree is listed without context. No mention of the actual skills needed (annotation tools, quality control, domain-specific knowledge). - No Salary or Benefits
Leaving out compensation is outdated. It makes candidates suspicious and filters out strong applicants who expect transparency. - Dismissive Hiring Process
“Only shortlisted candidates will be contacted” makes candidates feel like a number, not a person. This cold, one-way process damages your employer brand. - Zero Personality in the CTA
“Send your CV” is transactional and uninspiring. There’s no warmth, no encouragement, and no explanation of what the process will look like.
👉 This kind of job post does more harm than good. It’s uninspired, fails to sell the opportunity, and signals a poor candidate experience.
Bonus Tips to Make Your Data Annotator Job Post Stand Out
Even a well-written job description can get lost if it looks and feels like everything else out there. Here are a few advanced touches that will make your Data Annotator job post more appealing, trustworthy, and memorable:
🔒 1. Add a Security & Privacy Notice
Data annotation often involves sensitive information—medical images, financial documents, proprietary datasets. Candidates want reassurance that your hiring process is safe and ethical.
- Example: “We take applicant privacy seriously. We will never request payment, banking information, or personal financial details during any stage of our hiring process.”
🗓️ 2. Highlight Leave Days & Flexibility
Annotators spend long hours on detailed, repetitive work. Time off and flexibility matter a lot for maintaining focus and accuracy.
- Example: “Enjoy up to 20 paid days off per year, plus local public holidays, so you can recharge and return focused.”
- If you offer flexible or async hours, highlight it—it’s a huge perk for global teams.
📈 3. Training & Growth Opportunities
Most candidates don’t want annotation to be a dead-end job. They want a career path—into QA, ontology design, or even ML ops.
- Example: “Start as an annotator, grow into quality assurance, team lead, or ontology specialist roles. We provide paid training and a $1,000 annual learning budget to support your growth.”
🎥 4. Include a Video from the Hiring Manager
This single addition instantly makes your post more human. A short 60-second Loom or YouTube video from the team lead explaining the role and why it matters can dramatically increase trust and application quality.
- Example: “Watch a quick hello from our Head of Data Operations [Insert Link].”
Here is an example that we used in our master guide on how to write a great job post description , you can check it out here https://www.loom.com/share/ba401b65b7f943b68a91fc6b04a62ad4
🤝 5. Showcase Your Candidate Experience Promise
Most annotators are used to sending applications into a black hole. Flip that narrative. Tell them you’ll respect their time.
- Example: “We reply to every application within two weeks. No ghosting. No endless waiting. If you apply here, you’ll always know where you stand.”
👉 Adding even 2–3 of these tips can turn a good job post into one that attracts thoughtful, motivated candidates who care about quality and consistency.
Should You Use AI to Write Job Descriptions?
These days, it feels like every company is using AI to write job posts. Tools like ChatGPT, Jasper, or even ATS platforms like Workable and Manatal now offer “one-click job description” features. But here’s the problem: AI on autopilot creates generic, lifeless job posts that don’t connect with real candidates.
❌ Why You Shouldn’t Rely on AI Alone
- Generic Output: AI defaults to cookie-cutter phrasing (“Responsibilities include… Requirements are…”) that makes your job post blend into the noise.
- Wrong Candidates: A bland post attracts people who apply to everything, not those who actually care about the role.
- Brand Damage: A job post is a first impression of your company. If it feels robotic, it sends the wrong message about your culture and values.
✅ The Smart Way to Use AI
AI is a powerful tool—but only if you feed it the right ingredients. Don’t outsource your thinking. Instead, use AI to polish and organize what you’ve already drafted.
Here’s how:
Step 1: Give AI Real Inputs
Provide context about your company, role, and values. Example:
“We’re Arcadia Vision, a computer vision startup helping retailers improve inventory tracking. We’re hiring a Data Annotator who will label retail product images and video. Our culture is calm, detail-focused, and growth-oriented. We offer remote work, flexible hours, and a $1,000 learning budget. Here are a few notes I’ve written about the role: [paste notes]. Please turn this into a warm, candidate-friendly job description.”
Step 2: Borrow From Great Examples
Show AI the type of job post you want (like the two good Data Annotator posts above) and tell it:
“Make this post sound similar in tone and structure.”
Step 3: Use AI for Refinement, Not Creation
Let AI:
- Rephrase for clarity.
- Suggest bullet formatting.
- Adjust tone (formal ↔ conversational).
- Highlight missing sections (e.g., salary, perks, candidate promise).
But always inject your voice, culture, and unique role details first.
👉 In short: AI is your editor, not your author. Use it to save time, not to replace authenticity.
Build a winning team—without the hiring headache. WorkScreen helps you hire fast, confidently, and without second-guessing.
Copy-Paste Job Description Templates for Quick Use
✅ Option 1: Conversational, Culture-First (Entry-Level / Trainable)
Job Title: Junior Data Annotator – Learn AI From the Ground Up at [Company Name]
💼 Location: [Remote/Hybrid/On-site] (HQ: [City, State]) 🕒 Type: [Full-Time/Part-Time] 💰 Salary Range: [${X},000 – ${Y},000]/year
🎥 Meet the Hiring Manager
Watch a 60-second hello from your future lead: [Loom/YouTube link]
Who We Are
[Company Name] helps [describe customers/industry in one line—e.g., retailers reduce waste, clinics speed diagnosis, fintechs spot fraud] by building reliable AI systems. Great AI starts with great data—and that’s why our annotation team matters. If you’re detail-oriented and curious about how AI works, we’ll train you on tools and quality standards.
What You’ll Do
- Label images/text/audio/video using step-by-step playbooks.
- Learn industry tools (e.g., Labelbox, CVAT, or in-house tools).
- Hit accuracy and consistency targets; flag unclear cases.
- Suggest improvements to guidelines where instructions are ambiguous.
What We’re Looking For
- Comfortable with computers; quick to learn new software.
- Patient, focused, reliable; takes pride in careful work.
- Clear written communication; follows detailed instructions.
Nice to Have
- Experience in admin, QA, cataloging, data entry, or support.
- Familiarity with spreadsheets/ticketing tools.
Perks & Benefits
- Paid training & onboarding with a dedicated mentor
- PTO + local public holidays + paid sick leave
- Home-office/equipment stipend
- [Health/Wellness stipend or Insurance, region-dependent]
- Annual learning budget for courses/certifications
Why This Role Is a Great Fit
- No prior experience required: we’ll teach you the tools and standards.
- Real impact: your work directly improves production AI models.
- Calm, supportive team: quality over chaos, growth over grind.
- Future-proof skills: build a foundation in AI data operations.
How to Apply
Apply via WorkScreen.io so we can evaluate everyone fairly: [insert WorkScreen link]. We review every application and keep you updated at each step. We will never ask for fees or financial details.
✅ Option 2: Structured Format (Experienced)
Job Title: Data Annotator – [Domain/Focus, e.g., Computer Vision/NLP] at [Company Name]
💼 Location: [Remote/Hybrid/On-site] (HQ: [City, State]) 🕒 Type: [Full-Time/Part-Time] 💰 Salary Range: [${X},000 – ${Y},000]/year
Job Brief
[Company Name] is hiring an experienced Data Annotator to deliver high-quality labeled datasets that train our [CV/NLP/Audio] models. You’ll collaborate with project managers and ML engineers to maintain accuracy, consistency, and clear handling of edge cases at scale.
Responsibilities
- Annotate datasets (images/text/audio/video) per detailed guidelines.
- Conduct QA passes; maintain inter-annotator consistency.
- Help refine ontologies, edge-case rules, and playbooks.
- Document ambiguous cases; propose guideline clarifications.
- Work efficiently with tools (e.g., Labelbox, CVAT, internal platforms).
Requirements
- 1–2+ years in data annotation/QA for AI/ML projects.
- Proven accuracy and throughput under production timelines.
- Familiarity with annotation platforms and spreadsheets/ticketing.
- Strong attention to detail; clear written communication.
Nice to Have
- Domain exposure (e.g., retail OCR, medical imaging, speech).
- Background in linguistics, cognitive science, QA, or related fields.
Perks & Benefits
- Competitive compensation + [bonus/variable pay]
- PTO + local holidays + paid sick leave
- Health, dental, vision (or monthly stipend)
- Home-office/equipment stipend
- Annual learning & development budget
How to Apply
Submit your application via WorkScreen.io: [insert WorkScreen link]. We respect your time, provide timely updates, and never request payments or sensitive financial information.
Let WorkScreen Handle the Next Step of Hiring
Writing a strong job post is the first step. But once the applications start rolling in, the real challenge begins: how do you quickly spot the best candidates without wasting hours on resumes and guesswork?
That’s where WorkScreen.io comes in.
🚀 How WorkScreen Helps You Hire Smarter
- Quickly identify your most promising candidates
WorkScreen automatically evaluates, scores, and ranks applicants on a performance-based leaderboard—making it easy to spot top talent, save time, and make smarter, data-driven hiring decisions.
- Run one-click skill tests
With WorkScreen, you can administer one-click skill tests to assess candidates based on real-world ability—not just credentials like résumés and past experience. This helps you hire more confidently and holistically.
- Eliminate low-effort applicants
WorkScreen automatically eliminates low-effort applicants who use AI Tools to apply, copy-paste answers, or rely on “one-click apply.” This way, you focus only on genuine, committed, and high-quality candidates—helping you avoid costly hiring mistakes.
- Build a fair and transparent process
Every candidate goes through the same structured evaluation. No bias, no guesswork, no shortcuts—just a clear, skills-first approach that helps you avoid costly hiring mistakes.
Once your Data Annotator job post is live, use WorkScreen to take applicants through a fair, skills-based evaluation. In just a few clicks, you’ll have a ranked list of your top candidates—ready for interviews.
FAQ
Look for a mix of technical and soft skills:
- Attention to detail: Accuracy matters more than speed.
- Consistency: Ability to apply the same standard across thousands of samples.
- Tool proficiency: Familiarity with annotation platforms like Labelbox, CVAT, or Supervisely.
- Time management: Staying productive during repetitive tasks.
- Communication skills: Reporting unclear cases and suggesting guideline improvements.
- Confidentiality: Handling sensitive datasets responsibly.
Salaries vary by region, industry, and experience:
- Entry-level (no prior experience): Around $15–$20 per hour.
- Experienced annotators: $40,000–$55,000 per year.
- Specialized annotators (medical imaging, NLP, or QA roles): $60,000+ annually.
Remote-first companies may adjust rates based on cost of living.
No. Most companies don’t require a formal degree—only strong attention to detail, reliability, and ability to learn tools quickly. However, a background in linguistics, computer science, or data-related fields can be a plus for specialized projects.
The terms are often used interchangeably. In some contexts, “annotator” implies more complex tasks (like bounding boxes, transcription, or ontology design), while “labeler” refers to simpler tagging work. Ultimately, both roles prepare data for machine learning.
