Data Annotation (Spanish Speaker)

AI Trainer Jobs; United States; Remote; USD 40,000 - 54,000; Full time

Job summary

AI Trainer Jobs is seeking a Data Annotation (Spanish Speaker) contractor to remotely evaluate Spanish outputs against native-speaker standards. You will review fluency, register, and cultural-context, and write structured rationale for the modeling team to act on in the next training cycle.

Multilingual model quality relies on reviewers who read Spanish like a native speaker. This role requires native-speaking edits and the ability to apply rubrics consistently across evaluation batches, with

Qualifications

• Native or near-native Spanish fluency with strong written-English ability.

Responsibilities

• Evaluate Spanish model responses for fluency, accuracy, register, and cultural fit for Data Annotation (Spanish Speaker) assignments.

Skills

Multilingual evaluation

Native-speaker review

Rubric application

LLM evaluation

Spanish annotation

Education

Spanish linguistics background

Tools

TMS tools

MQM taxonomies

QA frameworks

LLM rubrics

Job description

Data Annotation (Spanish Speaker) is a remote Spanish specialist track for evaluating spanish annotation outputs against native-speaker standards. Reviewers spot fluency, register, and cultural-context errors that automated checks miss, and write structured rationale that the modeling team can act on in the next training cycle.

Category: Language, Localization & Culture · Pay: $34 / hr · Location: Remote - US-eligible · Contractor

Data Annotation (Spanish Speaker) is a remote Spanish specialist track for evaluating spanish annotation outputs against native-speaker standards.

About the role

Data Annotation (Spanish Speaker) is a remote Spanish specialist track for evaluating spanish annotation outputs against native-speaker standards. Reviewers spot fluency, register, and cultural-context errors that automated checks miss, and write structured rationale that the modeling team can act on in the next training cycle.
Multilingual model quality depends on reviewers who can read Spanish the way a native speaker does. AuraOne uses these reviewers to set the bar for tone, formality, and cultural register before a model ever ships to Spanish users.
Review translation and localization work. Dialect and cultural context matter here.

Responsibilities

• Evaluate Spanish model responses for fluency, accuracy, register, and cultural fit for Data Annotation (Spanish Speaker) assignments.
• Compare paired spanish annotation outputs and pick the stronger response with a written rationale.
• Flag hallucinations, code-switching errors, and formality mismatches with task-specific severity tags.
• Capture native-speaker edits that demonstrate the correct phrasing alongside each issue.

Role details

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

What you should bring

• Native or near-native Spanish fluency with strong written-English ability for Data Annotation (Spanish Speaker) work.
• Demonstrable experience editing, translating, or evaluating Spanish content.
• Comfort applying multi-page rubrics consistently across long evaluation batches.
• Clear written reasoning that names the issue and the standard being applied.
• Reliable async availability for at least 10 hours per week.
• Prior model-evaluation, annotation, or human-rater experience is a plus.

Example tasks

• Score a pair of Spanish responses to a customer-support prompt and pick the better one with a 1-2 sentence rationale.
• Flag formality mismatches in spanish annotation outputs (T/V usage, honorifics, regional register).
• Rewrite a model's Spanish response so it reads naturally and tag the original error category.
• Audit a 50-row evaluation batch for rubric consistency and surface drift back to the program lead.

Useful experience

• Background in Spanish linguistics, journalism, or translation studies.
• Experience with TMS tools, MQM error taxonomies, or other structured QA frameworks.
• Familiarity with LLM evaluation rubrics and inter-rater agreement workflows.

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

• Multilingual evaluation
• Native-speaker review
• Rubric application
• LLM output evaluation
• Spanish annotation

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.