Screenshot Understanding Dataset QA Specialist

AI Trainer Jobs; United States; Remote; USD 110,000 - 165,000; Full time

Benefits offered by this job

Remote work

Hourly pay $80–$120

Job summary

AI Trainer Jobs is seeking a remote contractor for Screenshot Understanding Dataset QA Specialist work. You will review prompts and model outputs, apply a global rubric, and provide structured feedback to help retrain the system.

You will compare responses, tag edge cases, and document rationale and rubric references. This role requires reliable, async availability for about 10 hours per week and strong attention to detail.

Qualifications

• Prior evaluation, annotation, or human-rater experience on screenshot understanding dataset QA.
• Ability to apply rubrics consistently across long batches.
• Strong written reasoning that names the issue and the rubric clause applied.
• Reliable async availability for at least 10 hours per week.

Responsibilities

• Evaluate model outputs against a versioned rubric and assign severity tags.
• Compare paired responses and select 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.

Skills

Evaluation experience

Annotation experience

Clear written reasoning

Attention to detail

Remote work commitment

Job description

About the role

Screenshot Understanding Dataset QA Specialist is a remote evaluation track for reviewing screenshot understanding dataset qa 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: Data Quality, Taxonomy & Ontology · Pay: $80–$120 / hr · Location: Remote — US-eligible · Contractor

Screenshot Understanding Dataset QA Specialist is a remote evaluation track for reviewing screenshot understanding dataset qa evaluation prompts and responses against AuraOne's quality rubric.

About the role

Screenshot Understanding Dataset QA Specialist is a remote evaluation track for reviewing screenshot understanding dataset qa 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 screenshot understanding dataset qa evaluation outputs into auditable labels, rationales, and regression cases for AuraOne Human Data.
Design and audit label taxonomies, knowledge graphs, and evaluation datasets.

Responsibilities

• Evaluate screenshot understanding dataset qa evaluation model outputs against a versioned rubric and assign severity tags for Screenshot Understanding Dataset QA Specialist 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.

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 screenshot understanding dataset qa evaluation or adjacent content for Screenshot Understanding Dataset QA Specialist 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 screenshot understanding dataset qa 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
• Screenshot Understanding Dataset QA evaluation
• Multimodal evaluation
• Cross-modal reasoning
• Grounding review
• Screenshot
• Understanding

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.