Dialect Code-Switching AI Evaluator

AI Trainer Jobs; United States; Remote; USD 28,000 - 55,000; Part time

Job summary

AI Trainer Jobs is seeking a Dialect Code-Switching AI Evaluator to remotely review prompts and model outputs. You will compare paired responses, label edge cases, and provide structured feedback for retraining; this role focuses on dialect code-switching evaluation and quality control in a US-eligible remote setting.

The contractor position requires strong attention to detail, ability to apply rubrics consistently, and clear written rationales.

Qualifications

• Experience evaluating prompts and responses.
• Ability to apply rubrics consistently across batches.
• Strong written reasoning to name issues and rubric clauses.

Responsibilities

• Evaluate dialect code switching AI outputs against a versioned rubric and assign severity tags.
• Compare paired responses and select the stronger answer with rationale.
• Label hallucinations, instruction-following failures, and unsafe content with structured tags.
• Capture ambiguous prompts and route to rubric updates.
• Calibrate reviewer scores against gold-standard examples weekly.

Skills

Dialect Code Switching

Annotation experience

Inter-rater calibration

Quality rubric

Job description

Dialect Code-Switching AI Evaluator is a remote evaluation track for reviewing dialect code switching ai 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

Dialect Code-Switching AI Evaluator is a remote evaluation track for reviewing dialect code switching ai evaluation prompts and responses against AuraOne's quality rubric.

About the role

Dialect Code-Switching AI Evaluator is a remote evaluation track for reviewing dialect code switching ai 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 dialect code switching ai 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 dialect code switching ai evaluation model outputs against a versioned rubric and assign severity tags for Dialect Code-Switching 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 dialect code switching ai evaluation or adjacent content for Dialect Code-Switching 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.

Example tasks

• Compare two dialect code switching ai 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
• Dialect Code Switching AI evaluation
• Localization review
• Cultural context
• Language evaluation
• Dialect
• Code
• Switching

Application boundary

Creating a specialist profile records your experience and preferences. Starting role intake is a separate action that attaches this role to your candidate record.

Specialist intake

The intake preserves your chosen role, the visible terms, and source attribution for reviewer context.

• 01 Confirm profile and eligibility details.
• 02 Attach this role deliberately.
• 03 Receive a human review decision or follow-up.

Placement timing depends on program demand and reviewer confirmation.