Embodied Navigation Failure Case Reviewer

AI Trainer Jobs; United States; Remote; USD 28,000 - 41,000; Full time

Benefits offered by this job

Remote work

Contractor role

Job summary

AI Trainer Jobs is seeking a remote Embodied Navigation Failure Case Reviewer to assess prompts and responses against AuraOne's rubric. You will compare outputs, label edge cases, and produce structured feedback to retrain the model.

As an independent contractor, the hourly rate will be confirmed after the interview. The role requires US eligibility for remote work and a commitment of approximately 10 hours per week.

Qualifications

• Prior evaluation, annotation, or human-rater experience on embodied navigation failure case evaluation.
• Clear written reasoning that names the issue and the rubric clause being applied.
• Reliable async availability for at least 10 hours per week.

Responsibilities

• Evaluate embodied navigation failure case evaluation model outputs against a versioned rubric and assign severity tags.
• 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.

Job description

Embodied Navigation Failure Case Reviewer is a remote evaluation track for reviewing embodied navigation failure case 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

Embodied Navigation Failure Case Reviewer is a remote evaluation track for reviewing embodied navigation failure case evaluation prompts and responses against AuraOne's quality rubric.

About the role

Embodied Navigation Failure Case Reviewer is a remote evaluation track for reviewing embodied navigation failure case 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 embodied navigation failure case 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 embodied navigation failure case evaluation model outputs against a versioned rubric and assign severity tags for Embodied Navigation Failure Case 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 embodied navigation failure case evaluation or adjacent content for Embodied Navigation Failure Case 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 embodied navigation failure case 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
• Embodied Navigation Failure Case evaluation
• Robotics evaluation
• Embodied AI
• Safety review
• Embodied
• Navigation

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.