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Annotation Labelling information

What is annotation labelling?

Annotation labelling is the process of tagging or marking data—such as images, text, or audio—with relevant information or labels. This is an essential step in preparing datasets for machine learning and artificial intelligence models, as it helps algorithms understand and learn from raw data. Annotation labelling can include tasks like identifying objects in photos, transcribing speech, or categorizing text. Skilled annotators ensure accuracy and consistency to improve model performance. People in this role often use specialized tools or software to streamline and standardize the annotation process.

What are the key skills and qualifications needed to thrive as an Annotation Labelling Specialist, and why are they important?

To thrive as an Annotation Labelling Specialist, you need strong attention to detail, data analysis capabilities, and familiarity with data annotation standards, usually supported by a background in computer science or related fields. Proficiency with annotation tools such as Labelbox, CVAT, or Supervisely, and sometimes knowledge of basic programming or scripting, is typically required. Excellent communication, consistency, and the ability to follow complex instructions are crucial soft skills for producing high-quality labeled data. These skills ensure the accuracy and reliability of datasets, which are foundational for successful machine learning and AI model development.

What are some common challenges faced by Annotation Labelling professionals, and how can they be managed?

Annotation Labelling professionals often encounter challenges such as maintaining high accuracy while handling repetitive data, meeting tight deadlines, and adapting to evolving project guidelines. To manage these, it’s important to develop strong attention to detail, regularly communicate with team leads to clarify instructions, and leverage annotation tools efficiently. Collaborating closely with quality assurance teams can also help identify and correct errors early, ensuring consistently high-quality outputs.

What is the difference between Annotation Labelling vs Data Labeling Specialist?

AspectAnnotation LabellingData Labeling Specialist
CredentialsBasic technical skills, attention to detailSimilar skills, sometimes additional domain knowledge
Work EnvironmentData annotation platforms, remote or officeData annotation tasks, often remote or in-office
Industry UsageAI, machine learning, autonomous vehiclesAI, machine learning, healthcare, retail
Search & ComparisonCommonly compared for entry-level data tasksRelated but broader role

Annotation Labelling involves marking data such as images, text, or videos to train AI models. Data Labeling Specialists perform similar tasks but may have a broader scope, including verifying and managing labeled data. Both roles are essential in AI development, often overlapping in skills and work environment, but Annotation Labelling is more focused on the annotation process itself.

What are popular job titles related to Annotation Labelling jobs in Florida? For Annotation Labelling jobs in Florida, the most frequently searched job titles are:
What job categories do people searching Annotation Labelling jobs in Florida look for? The top searched job categories for Annotation Labelling jobs in Florida are:
What cities in Florida are hiring for Annotation Labelling jobs? Cities in Florida with the most Annotation Labelling job openings:
Annotation Labelling jobs near you
Taxonomy Analyst (Spanish Speaker) ID76264

Taxonomy Analyst (Spanish Speaker) ID76264

AgileEngine

Miami, FL • On-site

Full-time

Posted 17 days ago

Job description


AgileEngine is an Inc. 5000 company that creates award-winning software for Fortune 500 brands and trailblazing startups across 17+ industries. We rank among the leaders in areas like application development and AI/ML, and our people-first culture has earned us multiple Best Place to Work awards.
WHY JOIN US
If you're looking for a place to grow, make an impact, and work with people who care, we'd love to meet you!
ABOUT THE ROLE
We are looking for a Taxonomy Analyst with Spanish fluency to review, annotate, and classify high-volume job and resume datasets according to strict taxonomy guidelines across Spain, Mexico, and the US Spanish market. You will standardize job titles, map occupations, validate metadata, and perform systematic data cleanup while ensuring linguistic accuracy and regional relevance across different Spanish dialects and market contexts. The role requires outstanding attention to detail, sharp pattern recognition, and the ability to maintain high-quality data standards.
WHAT YOU WILL DO
- Review, annotate, and classify high-volume datasets (job descriptions, resumes, and candidate profiles) aligning with established taxonomy guidelines;
- Support taxonomy operations by standardizing job titles, mapping occupations, and validating complex metadata to ensure consistent and accurate system outputs;
- Identify linguistic or structural data patterns;
- Perform systematic data cleanup;
- Handle validation tasks on large-scale datasets;
- Cross-reference, adapt, and validate multilingual content to ensure regional relevance across different Spanish dialects and market contexts;
- Communicate data insights;
- Track edge cases;
- Provide written feedback on guidelines in English via Slack and team syncs.
MUST HAVES
- You must be authorized to work for ANY employer in the US (e.g., Green card holders, TN visa holders, GC EAD, H4 EAD, U4U with EAD), as we are unable to sponsor or take over employment visa sponsorship at this time;
- 3+ years of proven experience in taxonomy, metadata management, content classification, data validation, or data labeling/annotation;
- Native-level fluency in Spanish;
- Strong professional communication skills in English (written and spoken) for daily team collaboration and data feedback;
- Outstanding attention to detail, sharp pattern recognition skills, and the ability to exercise sound judgment when handling ambiguous data points;
- Extensive experience reviewing and navigating high-volume, complex datasets.
NICE TO HAVES
- Deep market knowledge of the professional and recruitment landscapes in Spain, Mexico, and/or the US Hispanic market;
- Specialized background or knowledge in HR / job taxonomy (occupational mapping, labor market frameworks);
- Proficiency with Excel, Google Sheets, or basic SQL queries;
- Hands-on experience working with LLMs / AI tools (e.g., ChatGPT, Gemini, Claude) for data curation, model evaluation, or prompt validation.
PERKS AND BENEFITS
- Professional growth: Mentorship, TechTalks, and personalized growth roadmaps.
- Competitive compensation: USD-based pay with education, fitness, and team activity budgets.
- Exciting projects: Modern solutions with Fortune 500 and top product companies.
- Flextime: Flexible schedule with remote and office options.
Meet Our Recruitment Process
Application → Coding Challenge → Video Interview → Technical Interview or Hiring Manager Interview
Each step helps us understand your skills and overall fit.
If it's a match, you'll receive an offer.

Apply