AI Response Labeler / Annotator

About the Role

We’re looking for an English-language AI Response Labeler / Annotator to evaluate the quality of AI-generated responses. This role calls for strong English comprehension, analytical judgment, and the ability to apply detailed guidelines consistently across a high volume of work.

You’ll compare responses generated by different AI models and determine which one better meets a user’s needs. You’ll consider factual accuracy, reasoning, relevance, completeness, instruction following, safety, clarity, tone, and overall usefulness. The prompts, responses, annotation guidelines, training, and written evaluation work for this role are in English.

What You'll Do

• Perform side-by-side comparisons of AI-generated responses and select the stronger response using established evaluation criteria.
• Assess responses for factual accuracy, relevance, completeness, reasoning, instruction following, clarity, safety, tone, and usefulness.
• Evaluate varied tasks, including questions and answers, web-search results, file-based and image-based responses, content generation, and single-turn or multi-turn conversations.
• Identify meaningful differences between responses, such as unsupported claims, missed instructions, weak reasoning, and incomplete answers.
• Apply detailed, scenario-specific guidelines and make sound decisions when an example does not provide an obvious answer.
• Write concise, evidence-based explanations for your decisions when required.
• Meet established productivity expectations while maintaining accuracy and consistent judgment.
• Participate in training, guided practice, calibration, qualification reviews, and ongoing quality reviews.
• Incorporate feedback as evaluation guidelines and quality standards evolve.

What You'll Bring

• Excellent written English comprehension and communication skills, including the ability to read complex prompts and guidelines and explain evaluation decisions clearly.
• Strong critical-thinking skills and the ability to assess content across a wide range of topics.
• Sound judgment when evaluating factuality, reasoning, user intent, and subtle differences in response quality.
• Excellent attention to detail and the ability to apply structured criteria consistently.
• Comfort with repetitive, focused work and a high volume of evaluations.
• Ability to work independently, respond to feedback, and stay aligned with shared quality standards.

Preferred Qualifications

• Experience evaluating, ranking, or comparing AI-generated responses, particularly through side-by-side evaluation.
• Experience with data annotation, content quality assessment, search relevance evaluation, or model-quality review.
• Experience working with detailed rubrics, annotation guidelines, or quality benchmarks.
• Experience writing clear rationales that support evaluation decisions.

Work Pace and Productivity Expectations

This is a highly structured and repetitive role that involves completing similar evaluation tasks throughout the workday. Candidates should be comfortable maintaining focus, accuracy, and consistent judgment while reviewing a high volume of AI-generated content.

Most evaluation tasks are expected to take approximately 15 minutes, and employees are generally expected to complete a minimum of 25 tasks per day. Some tasks may take more or less time depending on their complexity.

Success in this role requires balancing productivity with quality. Employees must meet established daily expectations while carefully applying annotation guidelines and providing accurate, well-supported evaluation decisions.

Training and Qualification

All new hires must successfully complete a structured onboarding and qualification program before beginning production work.

The program includes training sessions, guided practice exercises, calibration against established quality benchmarks, and a formal qualification review.

Training is intended to establish consistent evaluation judgment across the team. Language fluency alone will not be sufficient to qualify. Employees must also demonstrate the ability to evaluate broader response quality, follow detailed annotation guidelines, explain their decisions, and complete work within the expected timeframe.

Employees will continue to receive feedback, quality reviews, and calibration support after entering production.

Compensation

The estimated compensation range is USD $2,200–$2,500 per month ($26,400–$30,000 annually). Actual compensation will depend on the hiring location, experience, skills, and internal equity. Compensation may be paid in local currency through the applicable local Professional Employer Organization (PEO) or Employer of Record (EOR) partner.

Location and Employment Structure

This is a remote role open to candidates in Latin American countries. Employment will be arranged through a local PEO or EOR partner, as applicable. Payroll, statutory benefits, and employment terms will follow the requirements of the candidate’s hiring country and the terms of their employment.

During the approximately 30-day training and qualification period, employees must work from 9:00 a.m. to 5:00 p.m. Pacific Time. After successfully completing training, employees may work standard business hours within their local time zone.

Originally posted on Himalayas