Multimodal Signals

Multimodal Signals is a remote evaluation track for reviewing multimodal signals evaluation prompts and responses against Aura One'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:
Multimodal Evaluation
· Pay: $40 / hr
·

Location:

Remote — US-eligible
· Contractor

Multimodal Signals is a remote evaluation track for reviewing multimodal signals evaluation prompts and responses against Aura One's quality rubric.

Multimodal Signals is a remote evaluation track for reviewing multimodal signals evaluation prompts and responses against Aura One'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 multimodal signals evaluation outputs into auditable labels, rationales, and regression cases for Aura One Human Data.
Judge model behavior on text, images, and documents. Also on mixed-input tasks.

• Evaluate multimodal signals evaluation model outputs against a versioned rubric and assign severity tags for Multimodal Signals 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.

• Prior evaluation, annotation, or human-rater experience on multimodal signals evaluation or adjacent content for Multimodal Signals 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.

• Compare two multimodal signals 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.

• 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.

$40 / hr
Expected arrangement: contractor , with program-defined task volume and review pacing. Placement depends on current program demand and reviewer confirmation.

• Model output evaluation
• Rubric-based annotation
• Severity tagging
• Inter-rater calibration
• Multimodal Signals evaluation

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

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.