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AI Trainer Specialist

Innodata

Data Annotation & Labeling Freelancer · Part-time
India June 9, 2026

Job description

About the Role

Innodata is looking for sharp, analytical, and language-proficient Data Annotators to review, evaluate, and annotate digital content. You will play a direct part in training and improving Large Language Models (LLMs) and AI responses.

Key Responsibilities

  • Review digital content to evaluate quality, grammar, coherence, and accuracy
  • Help improve AI model responses by providing detailed, logical annotations and feedback
  • Follow strict project guidelines and specific taxonomies while labeling and structuring data
  • Identify factual inconsistencies, errors, or inappropriate content in model outputs

Requirements

  • Strong English skills — B2+, C1, or C2 proficiency required
  • Strong attention to detail and excellent written communication
  • Good knowledge of current events, pop culture, and content categorization
  • Familiarity with social media and modern digital platforms

Assessment Process

  • General English Assessment + Task Readiness Assessment
  • Pearson Versant Test (B2+ required to pass)

Engagement Details

  • Remote freelancing, 5–6 hours daily
  • Pay rate: ₹350 per hour
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Frequently asked questions

Is Innodata legitimate?
Yes, Innodata is a legitimate, publicly traded company (NASDAQ: INOD) that has been in operation since 1988. Listed on RemoWork, Innodata serves major technology companies and enterprises worldwide. As always, we recommend verifying details on their official website and checking user reviews before applying.
How do I get started with Innodata?
Visit Innodata's official careers page to browse open positions. Create an account, submit your application with relevant qualifications and experience, and complete any required assessments (typically a General English test and Versant proficiency test). Some roles may involve additional technical evaluations or interviews.
Does Innodata offer remote work?
Yes, Innodata offers fully remote positions, particularly for data annotation, AI training, and Generative AI Associate roles within the US (some state exclusions may apply). The company also has on-site positions at offices in the Philippines, India, Sri Lanka, and other global locations.
How does Innodata pay its workers?
Innodata offers competitive compensation that varies by role, experience level, and location. Full-time employees receive standard payroll with benefits. Freelance and contract workers are compensated based on project terms. Specific payment details are discussed during the hiring process.
What skills do I need to work with Innodata?
Required skills vary by role. Data annotators need strong English, attention to detail, and the ability to follow complex guidelines. AI engineers need Python proficiency, ML framework experience, and relevant degrees. All roles value analytical thinking, adaptability, and a passion for AI and data quality.
What types of annotation tasks does Innodata offer?
Innodata provides a comprehensive range of annotation services including image annotation, text classification, audio transcription, video labeling, semantic segmentation, bounding box annotation, named entity recognition, sentiment analysis, optical character recognition, entity linking, syntactic parsing, and LLM response evaluation. Tasks cover both structured and unstructured data across 40+ languages.
What is the hiring process like at Innodata?
The hiring process typically includes submitting an application, completing a General English proficiency test and a Versant assessment (evaluating spoken and written English), followed by interviews assessing educational background, professional experience, and domain expertise. Some roles include practical assessments or annotation trials. The process evaluates both qualifications and cultural fit.
Is Innodata a publicly traded company?
Yes, Innodata is publicly traded on the NASDAQ stock exchange under the ticker symbol INOD. Founded in 1988, the company has a long track record in data services and has expanded significantly into AI data engineering. Being publicly traded means greater transparency through regular SEC filings and financial disclosures.
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