Data-Video Generalist (US-based)

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

Job summary

AI Trainer Jobs is seeking a Data-Video Generalist (US-based) for a remote evaluation track. You will review data video generalist prompts and responses against AuraOne's rubric, compare paired outputs, label edge cases, and provide structured feedback to retrain the model.

As a contractor, you will assess model outputs, tag content for policy and quality, and calibrate judgments against gold-standard examples on a weekly cadence.

Qualifications

• Prior evaluation, annotation, or human‑rater experience on data video generalist evaluation or adjacent content.
• Clear written reasoning that names issues and rubric clauses.
• Comfort applying multi‑page rubrics to long batches.

Responsibilities

• Evaluate data video generalist evaluation model outputs against a versioned rubric and assign severity tags.
• Compare paired responses and select the stronger answer with a rationale.

Skills

Data‑video evaluation

Rubric‑based annotation

Inter‑rater calibration

Attention to detail

Written reasoning

Async availability

Job description

Data-Video Generalist (US-based) is a remote evaluation track for reviewing data video 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: Video, Vision & Spatial AI · Pay: Hourly rate confirmed after the interview process · Location: Remote — US-eligible · Contractor

Data-Video Generalist (US-based) is a remote evaluation track for reviewing data video generalist evaluation prompts and responses against AuraOne's quality rubric.

About the role

Data-Video Generalist (US-based) is a remote evaluation track for reviewing data video 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 data video generalist evaluation outputs into auditable labels, rationales, and regression cases for AuraOne Human Data.
Review image and video output. Judge spatial reasoning and scene understanding.

Responsibilities

• Evaluate data video generalist evaluation model outputs against a versioned rubric and assign severity tags for Data-Video Generalist (US-based) 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 data video generalist evaluation or adjacent content for Data-Video Generalist (US-based) 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 data video 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
• Data Video Generalist evaluation
• Design

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.