Hardware Engineer, Robotics Lab

AI Trainer Jobs; United States; Remote; USD 55,000 - 83,000; Part time

Job summary

AI Trainer Jobs is seeking a Hardware Engineer for a Robotics Lab remote review role. You will evaluate robotics outputs against real-world physical-AI constraints, grading trajectory choices, perception assumptions, and safety envelopes.

The role emphasizes documenting corrected behavior to train the modeling team. The position is a contractor, remote within the US, with hourly compensation determined after the interview and program needs.

Qualifications

• Hands-on experience with robotic, autonomous, or teleoperated systems.
• Comfort reasoning about perception, motion planning, and physical safety envelopes.
• Familiarity with structured incident taxonomies and severity scoring.

Responsibilities

• Review robotics review outputs against safety standards and operating envelopes for Hardware Engineer, Robotics Lab 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.

Job description

Hardware Engineer, Robotics Lab 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.

Category: Robotics & Embodied AI · Pay: Hourly rate confirmed after the interview process · Location: Remote — US-eligible · Contractor

Hardware Engineer, Robotics Lab is a remote review track for evaluating robotics review outputs against real-world physical-AI constraints.

Role details

About the role

Hardware Engineer, Robotics Lab 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 Hardware Engineer, Robotics Lab 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.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 Hardware Engineer, Robotics Lab 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