[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"job-robotics-jobs-ecuador":3},{"Ques":4,"Slug":22,"Header":23,"job_category":45},{"title":5,"content":6},"Frequently Asked Questions",[7,10,13,16,19],{"A":8,"Q":9},"Yes. The Remote Type is Remote, and the role is designed for distributed collaboration across engineering and evaluation workflows.","Is this a remote role?",{"A":11,"Q":12},"Modern robotics teams often use human feedback loops to improve perception datasets, evaluate autonomy behaviors, and assess robotics copilots. RLHF-style evaluation, prompt evaluation, and QA evaluation help ensure reliable outputs and model performance improvement.","Why does a robotics role mention RLHF, data labeling, and LLM evaluation?",{"A":14,"Q":15},"Common needs include computer vision annotation (2D\u002F3D boxes, segmentation, keypoints), multimodal alignment for sensor fusion, and structured labeling for events, failure modes, and safety-relevant behaviors.","What kinds of annotation are most relevant to robotics?",{"A":17,"Q":18},"This posting is FULL_TIME. Rex.zone may also list remote contract, freelance, entry-level, and senior openings depending on current hiring needs.","Do you offer contract or freelance options?",{"A":20,"Q":21},"You may see roles tied to AI labs, technology companies, tech startups, BPOs, and annotation vendors supporting robotics and autonomy programs.","Which industries typically hire for robotics roles listed on Rex.zone?","robotics-jobs-ecuador",{"desc":24,"title":25,"content":26},"Robotics jobs Ecuador on Rex.zone focus on mid-senior engineers who design, integrate, and validate robotics systems for real-world automation workflows. This remote, full-time role connects robotics engineering with modern AI\u002FML training pipelines, including data labeling, RLHF, QA evaluation, and prompt evaluation to improve perception, planning, and control. You will build reliable datasets for computer vision annotation, named entity recognition for logs and task text, and content safety labeling for human-in-the-loop review—supporting model performance improvement through training data quality and annotation guidelines compliance. Explore Rex.zone for remote robotics roles across AI labs, tech startups, and annotation vendors.","Robotics Jobs Ecuador",[27,30,33,36,39,42],{"h2":28,"desc":29},"Robotics Jobs Ecuador — Role Overview","Keyword: Robotics Jobs Ecuador | Job Title: Robotics Engineer (Ecuador Remote)\nDate: 25-02-2026 | Company: Rexzone | Country: US | Remote Type: Remote | Employment Type: FULL_TIME | Experience Level: Mid-Senior | Industry: Technology | Job Function: Engineering | Skills: Robotics, ROS, Computer Vision Annotation, Data Labeling, RLHF, QA Evaluation, LLM Evaluation, Prompt Evaluation, Training Data Quality, Annotation Guidelines, Sensor Fusion, SLAM, Motion Planning, Control Systems, Simulation, Python, C++, Git | Salary Currency: USD | Salary Min: 63360 | Salary Max: 126720 | Pay Period: YEAR\n\nYou will contribute to robotics programs by building and evaluating perception and autonomy datasets, improving annotation operations, and collaborating with cross-functional teams. The work includes defining labeling taxonomies for robotics perception, running QA audits, and using human feedback loops to improve model performance in navigation, manipulation, and safety-critical decisioning.\n\nKey responsibilities:\n- Define robotics data labeling specs for camera, LiDAR, and multimodal sensor fusion.\n- Manage computer vision annotation tasks (2D\u002F3D boxes, segmentation, keypoints) aligned to autonomy requirements.\n- Perform QA evaluation, inter-annotator agreement checks, and annotation guidelines compliance reviews.\n- Support RLHF-style evaluation and prompt evaluation for robotics copilots, fleet diagnostics, and operator tools.\n- Curate training datasets, analyze edge cases, and drive training data quality improvements for model performance improvement.\n- Partner with engineers and stakeholders to translate robotics requirements into measurable evaluation protocols.\n\nWho this is for:\n- Mid-senior robotics engineers or data-focused robotics specialists with strong ML-adjacent workflows.\n- Candidates familiar with ROS, simulation, and production-grade engineering practices.\n\nSearch modifiers covered:\n- Remote, full-time, contract, freelance, entry-level, senior\n- NLP, computer vision, content safety, LLM training\n- AI labs, tech startups, BPOs, annotation vendors",{"h2":31,"desc":32},"What You Will Work On","You will work on robotics perception and autonomy programs where high-quality labeled data and consistent evaluation are critical. Typical deliverables include:\n- Dataset development: sampling strategies, edge-case mining, and balanced splits for training\u002Fvalidation.\n- Evaluation design: QA scorecards, acceptance thresholds, and model regression tracking.\n- LLM\u002Fagent support: prompt evaluation, response grading, and RLHF-aligned feedback for robotics assistants.\n- Safety and compliance: content safety labeling and sensitive content handling protocols when reviewing operator logs, images, or text.\n- Tooling and process: improve annotation workflows, resolve ambiguity in guidelines, and reduce rework through clearer specs.",{"h2":34,"desc":35},"Minimum Qualifications","Requirements:\n- Mid-senior experience in robotics engineering, perception, autonomy, or adjacent ML operations.\n- Familiarity with ROS\u002FROS2 concepts, robotics sensor modalities, and real-world deployment constraints.\n- Experience with data labeling programs, QA evaluation, or structured review workflows.\n- Strong Python and\u002For C++ proficiency for analysis, tooling, or pipeline integration.\n- Ability to write clear annotation guidelines and drive annotation guidelines compliance across teams.",{"h2":37,"desc":38},"Preferred Qualifications","Nice to have:\n- Experience with SLAM, sensor fusion, motion planning, or control systems.\n- Background in computer vision annotation standards (COCO-style segmentation, 3D cuboids) and dataset governance.\n- Exposure to RLHF, LLM evaluation, prompt evaluation, and rubric-based grading.\n- Familiarity with content safety labeling, policy interpretation, and reviewer calibration.\n- Experience working with AI labs, tech startups, BPOs, or annotation vendors.",{"h2":40,"desc":41},"Compensation, Schedule, and Remote Work","Compensation:\n- Salary Range: USD 63360 to 126720 per YEAR\n\nWork model:\n- Remote Type: Remote\n- Employment Type: FULL_TIME\n\nNotes:\n- Final leveling and offer range depend on scope, domain depth, and evaluation\u002FQA ownership.",{"h2":43,"desc":44},"How to Apply on Rex.zone","Apply through Rex.zone by searching the keyword “Robotics Jobs Ecuador” and selecting the role that matches your robotics domain (perception, planning, simulation, or data\u002FQA operations). Prepare a resume highlighting robotics projects, datasets you’ve built or evaluated, QA methods you used, and measurable model performance improvement outcomes driven by training data quality.","Engineering"]