Hindi Localization AI Evaluator

AI Trainer Jobs; United States; Remote; USD 34,000 - 55,000; Full time

Job summary

AuraOne is seeking a Hindi Localization AI Evaluator to remotely assess generalist evaluation prompts and responses against its quality rubric. Reviewers compare paired outputs, label edge cases, and provide structured feedback to retrain the model.

The role is a contract, remote, US-eligible, paying an hourly rate after the interview. Responsibilities include evaluating model outputs, tagging content, and calibrating against gold standards to maintain reviewer quality.

Qualifications

• Prior evaluation, annotation, or human-rater experience on hindi generalist evaluation or adjacent content.
• Ability to apply multi-page rubrics consistently across long batches.
• Clear written reasoning that names the issue and rubric clause.
• Reliable async availability for at least 10 hours per week.

Responsibilities

• Evaluate hindi generalist evaluation model outputs against a rubric and assign severity tags.
• Compare paired responses and select the stronger answer with rationale.
• Label hallucinations, instruction-following failures, and unsafe content with tags.
• Flag ambiguous prompts and suggest rubric updates.

Skills

Model output evaluation

Rubric-based annotation

Severity tagging

Inter-rater calibration

Hindi generalist evaluation

Localization review

Cultural context

Language evaluation

Hindi

Localization

Job description

About the role

Hindi Localization AI Evaluator is a remote evaluation track for reviewing hindi generalist evaluation prompts and responses against AuraOne's quality rubric. Reviewers compare paired outputs, label edge cases, and write the kind of structured feedback the modeling team can use to retrain.

Category: Frontier Model Evaluation · Pay: Hourly rate confirmed after the interview process · Location: Remote — US-eligible · Contractor

Hindi Localization AI Evaluator is a remote evaluation track for reviewing hindi generalist evaluation prompts and responses against AuraOne's quality rubric.

About the role

Hindi Localization AI Evaluator is a remote evaluation track for reviewing hindi generalist evaluation prompts and responses against AuraOne's quality rubric. Reviewers compare paired outputs, label edge cases, and write the kind of structured feedback the modeling team can use to retrain.
AI data reviewers help turn hindi generalist evaluation outputs into auditable labels, rationales, and regression cases for AuraOne Human Data.
Review frontier model outputs. Judge benchmark failures and calibrate other evaluators.

Responsibilities

• Evaluate hindi generalist evaluation model outputs against a versioned rubric and assign severity tags for Hindi Localization AI Evaluator assignments.
• Compare paired responses and pick the stronger answer with a written rationale.
• Label hallucinations, instruction-following failures, and unsafe content with structured tags.
• Capture ambiguous prompts and route them back to the program team for rubric updates.
• Maintain reviewer-quality scores by calibrating against gold-standard examples each week.

Role details

Track Evaluation & annotation Work model Remote · Independent specialist contractor Compensation Hourly rate confirmed after the interview process. Eligible from US

What you should bring

• Prior evaluation, annotation, or human-rater experience on hindi generalist evaluation or adjacent content for Hindi Localization AI Evaluator work.
• Comfort applying multi-page rubrics consistently across long batches.
• Clear written reasoning that names the issue and the rubric clause being applied.
• Strong attention to detail and the ability to flag when a prompt itself is the problem.
• Reliable async availability for at least 10 hours per week.

Role signals

Example tasks

• Compare two hindi generalist evaluation model responses to the same prompt and pick the stronger one with rationale.
• Tag an unsafe response with the correct policy category and severity.
• Audit a 50-row batch for rubric consistency and report drift to the program lead.
• Propose a rubric clarification after spotting a recurring failure mode.

Useful experience

• Background in linguistics, content moderation, or trust & safety review.
• Experience with inter-rater agreement metrics and calibration cycles.
• Domain expertise that lets you spot subject-matter errors automated checks miss.

Compensation and schedule

Hourly rate confirmed after the interview process.
Expected arrangement: contractor , with program-defined task volume and review pacing. Placement depends on current program demand and reviewer confirmation.

Skills used in matching

• Model output evaluation
• Rubric-based annotation
• Severity tagging
• Inter-rater calibration
• Hindi generalist evaluation
• Localization review
• Cultural context
• Language evaluation
• Hindi
• Localization