Robotics Data Quality Reviewer

AI Trainer Jobs; United States; Remote; USD 34,000 - 83,000; Part time

Job summary

AI Trainer Jobs is seeking a Robotics Data Quality Reviewer for a remote contractor role in the United States. You will assess robotics outputs against perception, motion planning, and safety constraints while documenting corrections for model training.

The work involves evaluating teleoperation, navigation, and manipulation tasks with an emphasis on safety and reliability. Ideal candidates will bring hands-on robotics experience, strong analytical writing, and the ability to allocate at least

Qualifications

• Hands-on experience with robotic, autonomous, or teleoperated systems.
• Familiarity with structured incident taxonomies and severity scoring.
• Clear written reasoning that names the failure mode and the standard being applied.

Responsibilities

• Review robotics review outputs against current safety standards and operating envelopes for Robotics Data Quality Reviewer assignments.
• Grade perception, motion, and decision logic on a structured rubric.
• Flag near-miss and unsafe trajectories with severity tags that match incident taxonomies.
• Capture the corrected behavior or reference trajectory so the modeling team can train on it.

Skills

Robotics evaluation

Perception review

Motion safety

Incident taxonomy

Robotics review

Robotics and physical AI

AI evaluation

Rubric writing

Expert review

Job description

About the role

Robotics Data Quality Reviewer is a remote review track for evaluating robotics review outputs against real-world physical-AI constraints. Reviewers grade trajectory choices, perception assumptions, and safety envelopes; flag failure modes; and document the corrected behavior so the modeling team can train on it.
Robotics review systems run in the physical world, where a confident-looking failure can hurt someone. AuraOne uses experienced robotics and operations reviewers to grade outputs against perception, motion, and safety constraints.
Review teleoperation, navigation, and manipulation work. Physical-world safety is part of it.

Responsibilities

• Review robotics review outputs against current safety standards and operating envelopes for Robotics Data Quality Reviewer assignments.
• Grade perception, motion, and decision logic on a structured rubric.
• Flag near-miss and unsafe trajectories with severity tags that match incident taxonomies.
• Capture the corrected behavior or reference trajectory so the modeling team can train on it.

Role details

Track Robotics & physical AI review Work model Remote · Independent specialist contractor Compensation Hourly rate confirmed after the interview process. Eligible from US

What you should bring

• Hands-on experience with robotic, autonomous, or teleoperated systems. Operating, supervising, or evaluating them for Robotics Data Quality Reviewer work.
• Comfort reasoning about perception, motion planning, and physical safety envelopes.
• Familiarity with structured incident taxonomies and severity scoring.
• Clear written reasoning that names the failure mode and the standard being applied.
• Reliable async availability for at least 10 hours per week.

Role signals

Example tasks

• Grade a robotics review model's chosen trajectory and flag any near-miss or unsafe motion.
• Score a perception output against ground-truth labels and rate the failure category.
• Adjudicate a disputed safety call between two reviewers using firm operating procedures.
• Audit a 25-trajectory batch for rubric consistency and report drift to the program lead.

Useful experience

• Background in robotics engineering, autonomy operations, or industrial safety.
• Prior experience reviewing AI-assisted robotics tooling and its failure modes.
• Familiarity with simulation environments and trajectory-replay tooling.

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

• Robotics evaluation
• Perception review
• Motion safety
• Incident taxonomy
• Robotics review
• Robotics and physical AI
• AI evaluation
• Rubric writing
• Expert review