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Llm Annotation Jobs in Georgia (NOW HIRING)

Smarsh

Lead Data Scientist

Atlanta, GA

... LLM) solutions that power next-generation compliance and surveillance systems. You'll work on ... Data annotation and quality review * Exploratory data analysis and model fail state analysis

Smarsh

Lead Data Scientist

Atlanta, GA

... LLM) solutions that power next-generation compliance and surveillance systems. You'll work on ... Data annotation and quality review * Exploratory data analysis and model fail state analysis

All JobsLlm Annotation JobsLlm Annotation Jobs in Georgia

Llm Annotation information

Which 5 jobs will survive AI?

Jobs involving LLM annotation, such as data annotators and labelers, are likely to persist as they require human judgment for complex or nuanced tasks. Roles that involve creative thinking, emotional intelligence, and strategic decision-making, like psychologists, teachers, healthcare professionals, and managers, are also expected to remain in demand despite AI advancements. These jobs often require skills that are difficult for AI to replicate fully.

How much do AI annotators make?

AI annotators, including those working as language model annotation specialists, typically earn between $12 and $20 per hour, depending on experience, location, and the complexity of the tasks. Some positions may offer hourly wages or project-based pay, with higher rates for specialized skills or advanced tools proficiency.

Are data annotations still hiring?

Data annotation roles, including those for large language models (LLMs), are currently in demand as companies continue to develop AI and machine learning systems. These jobs often require attention to detail and familiarity with annotation tools, and opportunities are available through various online platforms and companies expanding their AI teams.

What is an LLM annotator?

An LLM annotator is a person who labels and tags data to train large language models (LLMs). They review and annotate text data to improve model accuracy, often using specialized tools and following specific guidelines. This role requires attention to detail and understanding of language patterns.

What is the difference between Llm Annotation vs Data Labeler?

AspectLlm AnnotationData Labeler
Required CredentialsBasic computer skills, sometimes familiarity with AI toolsBasic skills, often on-the-job training
Work EnvironmentRemote or office-based, tech-focusedRemote or on-site, varied industries
Industry UsageAI, machine learning, NLP projectsVarious industries including marketing, healthcare, and tech
Search & Comparison IntentUnderstanding roles in AI data preparationGeneral data labeling tasks

In summary, Llm Annotation involves specialized annotation for large language models, often requiring familiarity with AI tools, while Data Labeler is a broader role focused on labeling data across multiple industries with minimal technical requirements.

What is LLM annotation?

LLM annotation refers to the process of labeling or tagging data specifically for training and evaluating large language models (LLMs) like GPT or BERT. Annotators read text and apply labels, correct errors, or provide feedback to help improve the model's understanding and performance. This work is crucial for supervised learning, as well-annotated datasets help LLMs better recognize patterns, context, and meaning in human language. LLM annotation can involve tasks such as sentiment analysis, named entity recognition, or instruction following. Annotators often use specialized platforms or tools to complete their tasks efficiently and accurately.

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

To thrive as an LLM Annotation Specialist, you need strong analytical skills, attention to detail, and a background in linguistics, computer science, or a related field. Familiarity with annotation platforms, natural language processing (NLP) tools, and data labeling systems is typically required. Excellent communication, critical thinking, and the ability to follow guidelines precisely are valuable soft skills for this role. These skills ensure high-quality, accurate data annotation, which directly impacts the performance and reliability of large language models.

What are some common challenges faced by LLM Annotation specialists, and how can they be addressed?

LLM Annotation specialists often encounter challenges such as interpreting ambiguous language data, maintaining annotation consistency across complex datasets, and keeping up with evolving guidelines. These can be addressed by participating in regular team syncs to clarify guidelines, using annotation tools with built-in quality checks, and collaborating closely with project leads and fellow annotators. Continuous learning and open communication help ensure high-quality, reliable data annotation and support professional growth within the AI and NLP fields.
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Llm Annotation jobs near you
Infographic showing various Llm Annotation job openings in Georgia as of July 2026, with employment types broken down into 1% As Needed, 51% Full Time, 45% Part Time, 1% Temporary, and 2% Contract. Highlights an 46% Physical, 1% Hybrid, and 53% Remote job distribution.

Malayalam Linguistic QA Specialist (Remote)

Braintrust

Savannah, GA • Remote

$20 - $30/hr

Full-time

Posted yesterday

New

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
About this role

In this hourly, remote contractor role, you will work as a Malayalam Quality Assurance Lead (QAL) to oversee quality, consistency, and trainer performance across Malayalam AI training projects. You will review AI-generated Malayalam content and trainer/QA work, evaluate output quality against project guidelines, provide precise written feedback, and ensure that all contributors follow the expected quality standards.
You will assess work for accuracy, fluency, grammar, spelling, tone, cultural appropriateness, meaning preservation, instruction-following, formatting, and adherence to project-specific rubrics. You will spot recurring quality issues, communicate updates to trainers and QAs, support onboarding, maintain documentation, and help activate contributors who are not working consistently. This role requires strong Malayalam and English skills, excellent attention to detail, structured communication, and the ability to manage quality workflows across remote teams.
This role is with SME Careers, a fast-growing AI Data Services company and subsidiary of SuperAnnotate, delivering training data for many of the world’s largest AI companies and foundation-model labs. Your Malayalam quality leadership will directly help improve the world’s premier AI models by ensuring that Malayalam training data is natural, accurate, culturally appropriate, well-documented, and aligned with client expectations.
Selection process involves an AI interview, a domain-specific task, and an interview with a recruiter.

Your profile
  • Bachelor’s or Master’s degree in Malayalam, Linguistics, Translation, Communications, Journalism, English, Education, Quality Assurance, or a relevant domain/related field.
  • Native or near-native Malayalam proficiency with strong reading and writing skills.
  • Strong grasp of the English language to follow project guidelines, communicate with teams, and provide clear feedback in English.
  • 3+ years of professional experience in Malayalam writing, editing, translation, localization, content QA, AI training, education, annotation, or related language-review workflows.
  • Strong understanding of Malayalam grammar, spelling conventions, punctuation, tone, register, regional variation, and cultural context.
  • Ability to evaluate Malayalam content against detailed rubrics and identify issues such as mistranslation, literal phrasing, unnatural tone, hallucinated claims, ambiguity, or inconsistent terminology.
  • Experience leading or supporting remote teams of trainers, annotators, reviewers, editors, or QAs is strongly preferred.
  • Comfortable working in fast-moving remote environments using tools such as Discord, Google Sheets, Google Docs, trackers, dashboards, and project management systems.
  • Highly detail-oriented and organized, with the ability to maintain style guides, FAQs, trackers, onboarding materials, honeypots, and other quality documentation.
  • Experience with AI training, data annotation, large language models, prompt/response evaluation, or rubric-based LLM QA is a strong plus.
Key responsibilities
  • Quality monitoring: Spot-check Malayalam items, identify quality issues, provide ongoing feedback through DMs, and escalate recurring or critical issues.
  • Trainer and QA communication: Update trainers and QAs on Discord about new item guidelines, project changes, workflow updates, and quality expectations.
  • Question handling: Respond to trainer/QA questions clearly and promptly, especially around Malayalam wording, register, translation fidelity, cultural context, Kerala usage, diaspora usage, and edge cases.
  • Trainer/QA activation management: DM contributors who are inactive or not working, encourage activation, track follow-ups, and flag availability issues when needed.
  • Documentation: Create and maintain Malayalam project documentation, including style guides, trackers, FAQs, quality notes, examples, honeypots, and onboarding materials.
  • Onboarding and training: Schedule and run onboarding/training calls with trainers and QAs to explain project expectations, workflows, rubrics, quality standards, and Malayalam-specific style requirements.
  • Quality alignment: Ensure all trainers and QAs apply Malayalam language guidelines consistently and understand updates as projects evolve.
  • Process improvement: Identify recurring quality gaps, propose workflow improvements, and help build scalable QA processes for Malayalam-language projects.

Apply