Robotics Engineer; Remote

Robotics Engineer (Remote) — Rex.zone

Rex.zone connects Mid‑Senior engineers with full‑time remote robotics roles supporting real‑world automation and AI/ML training workflows. You will build and validate robotic perception and control systems while improving training data quality through data labeling, RLHF evaluation, prompt evaluation, and QA evaluation.

• Deliver perception and autonomy features using ROS/ROS2, Python, and C++ across simulation and real‑world testing
• Work on computer vision, sensor fusion, SLAM, and motion planning modules
• Define data requirements for model performance improvement, including labeling taxonomies (detection, tracking, depth, segmentation)
• Establish annotation guidelines compliance; perform computer vision annotation audits and inter‑annotator agreement checks using QA evaluation metrics
• Support RLHF evaluation and prompt evaluation workflows for language interfaces in robotic systems
• Partner with data labeling teams and vendors to scale throughput while maintaining training data quality
• Validate sensor fusion and SLAM outputs; triage dataset gaps; propose targeted collection and synthetic data strategies
• Contribute to content safety labeling for human‑robot interaction prompts, logs, and UI outputs when needed

• Mid‑Senior experience shipping robotics or autonomy systems in production or applied research
• Strong programming ability in Python and C++ with practical ROS/ROS2 experience
• Hands‑on understanding of computer vision, sensor fusion, SLAM, and motion planning fundamentals
• Experience with datasets for ML training: data labeling, QA evaluation, and error analysis
• Familiarity with RLHF, prompt evaluation, or LLM evaluation concepts in applied settings
• Ability to write clear specs, labeling instructions, and test plans for cross‑functional teams

$30–$50 per hour (USD), base salary.
Full‑time, Remote.

Apply via Rex.zone and include a brief summary of robotics projects (ROS nodes, perception models, SLAM pipelines, motion planning, or evaluation tooling) plus examples of dataset work (annotation guidelines, QA evaluation reports, RLHF evaluation contributions, or LLM evaluation analyses).