Lingala Bilingual Expert

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

Job summary

AuraOne is seeking a Lingala Bilingual Expert for remote evaluation. You will review lingala prompts and responses, compare outputs, and provide structured feedback to retrain the model. You will label edge cases and apply a versioned rubric to ensure auditability and quality in data labeling.

Responsibilities include selecting the stronger answer with rationale, tagging policy violations, and flagging ambiguities for rubric updates. This is a contractor, remote role with flexible hours.

Qualifications

• Prior evaluation, annotation, or human-rater experience in Lingala bilingual evaluation or related content.
• Ability to apply multi-page rubrics consistently across large batches.
• Clear written reasoning naming the issue and rubric clause being applied.
• Strong attention to detail and ability to flag prompt problems.

Responsibilities

• Evaluate Lingala bilingual evaluation model outputs against a versioned rubric and assign severity tags.
• Compare paired responses and select the stronger answer with a rationale.
• Label hallucinations, instruction-following failures, and unsafe content with structured tags.
• Capture ambiguous prompts and route them back to improve rubrics.
• Calibrate reviewer-quality scores against gold-standard examples weekly.

Job description

Lingala Bilingual Expert is a remote evaluation track for reviewing lingala bilingual 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

Lingala Bilingual Expert is a remote evaluation track for reviewing lingala bilingual evaluation prompts and responses against AuraOne's quality rubric.

About the role

Lingala Bilingual Expert is a remote evaluation track for reviewing lingala bilingual 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 lingala bilingual 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 lingala bilingual evaluation model outputs against a versioned rubric and assign severity tags for Lingala Bilingual Expert 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.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 lingala bilingual evaluation or adjacent content for Lingala Bilingual Expert 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 lingala bilingual 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
• Lingala Bilingual evaluation