Algorithmic Complexity Reasoning Reviewer

AI Trainer Jobs; United States; Remote; USD 34,000 - 62,000; Part time

Job summary

AI Trainer Jobs is seeking a remote Algorithmic Complexity Reasoning Reviewer to assess prompts and responses against AuraOne's rubric. You will compare outputs, label edge cases, and provide structured feedback for retraining the model.

This is a contractor role with hourly compensation to be confirmed after interview. Responsibilities include evaluating model outputs, tagging issues, and flagging ambiguous prompts.

Qualifications

• Prior evaluation, annotation, or human-rater experience on algorithmic complexity reasoning evaluation or adjacent content.
• 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.

Responsibilities

• Evaluate algorithmic complexity reasoning evaluation model outputs against a versioned rubric and assign severity tags for Algorithmic Complexity Reasoning Reviewer 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.

Skills

Evaluation experience

Annotation

Inter-rater calibration

Attention to detail

Written reasoning

Async availability

Tools

Rubric-based annotation

Job description

Algorithmic Complexity Reasoning Reviewer is a remote evaluation track for reviewing algorithmic complexity reasoning 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

Algorithmic Complexity Reasoning Reviewer is a remote evaluation track for reviewing algorithmic complexity reasoning evaluation prompts and responses against AuraOne's quality rubric.

About the role

Algorithmic Complexity Reasoning Reviewer is a remote evaluation track for reviewing algorithmic complexity reasoning 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 algorithmic complexity reasoning 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 algorithmic complexity reasoning evaluation model outputs against a versioned rubric and assign severity tags for Algorithmic Complexity Reasoning Reviewer 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 algorithmic complexity reasoning evaluation or adjacent content for Algorithmic Complexity Reasoning Reviewer 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 algorithmic complexity reasoning 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
  • Algorithmic Complexity Reasoning evaluation
  • Formal reasoning
  • Proof review
  • Quantitative analysis
  • Algorithmic
  • Complexity

  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.