Agricultural Robotics Computer Vision Engineer

Agricultural Robotics Computer Vision Engineer (Computer Vision Robotics Jobs) at Rex.zone focuses on building, training, and evaluating vision-based perception for field robotics, including crop and weed detection, fruit counting, row following, obstacle detection, and autonomous navigation. You will work in remote, full-time engineering workflows that connect data collection, labeling, model training pipelines, and quality assurance to measurable robot performance improvement in real-world farm environments. This role emphasizes computer vision annotation strategy, dataset curation, model evaluation, and deployment readiness for edge devices and robotics stacks used by AI labs, tech startups, and robotics teams recruiting through Rex.zone.

Job Overview

About Rex.zone

Rex.zone is a platform where employers and candidates connect around AI/ML and robotics work, including computer vision, NLP, LLM training pipelines, and evaluation roles. Teams hiring for agricultural robotics often need engineers who can translate messy field data into reliable perception models through dataset strategy, annotation guidelines, training data quality checks, and repeatable model evaluation.

What You Will Do

Design and improve computer vision pipelines for agricultural robots, including detection, segmentation, tracking, and depth-aware perception for crops, weeds, and obstacles. Define annotation guidelines and labeling schemas (bounding boxes, polygons, instance masks, keypoints) and ensure annotation guidelines compliance across datasets. Build dataset curation workflows: sampling strategy, class balance, hard-negative mining, and long-tail coverage for field conditions. Run training and evaluation loops, analyze error modes, and drive model performance improvement using metrics such as mAP, IoU, precision/recall, and confusion matrices. Collaborate with robotics engineers on integration with ROS2, sensor fusion, and on-robot inference constraints for edge deployment. Establish QA evaluation processes for training data quality, label consistency, and ground-truth reliability to reduce field failures.

Core Responsibilities

Perception modeling for agricultural robotics: implement and iterate on object detection and image segmentation models for field scenarios. Data operations for vision: oversee data labeling strategy, computer vision annotation standards, and dataset versioning for reproducible experiments. Evaluation and validation: create test sets, conduct offline evaluation, and support field validation for robustness across lighting, weather, occlusion, motion blur, and seasonal shifts. Tooling and automation: write scripts for dataset ingestion, preprocessing, augmentation, and automated QA checks; maintain experiment tracking for model comparisons. Cross-functional execution: work with hardware and autonomy teams to align perception outputs with navigation and manipulation requirements.

Required Qualifications

Mid-Senior engineering experience delivering computer vision systems or robotics perception models. Strong knowledge of object detection, semantic/instance segmentation, and model evaluation methodology. Practical experience with dataset creation, data labeling workflows, and training data quality processes for CV projects. Familiarity with robotics stacks and deployment constraints (e.g., ROS2, sensor fusion, real-time inference). Ability to communicate clear annotation guidelines, evaluation plans, and rollout criteria for production-like robotics environments.

Preferred Qualifications

Experience with agricultural domain data (orchards, row crops, harvest robotics, weeding robots) and field data challenges. Hands-on experience with visual SLAM, depth sensing, multi-camera calibration, or LiDAR-camera fusion. Experience deploying models to edge devices and optimizing inference latency and memory. Knowledge of active learning, hard example mining, and dataset shift monitoring for long-running robotics programs. Experience collaborating with annotation vendors or internal labeling teams and designing QA sampling plans.

Tools And Workflows You Will Use

Typical workflows include data collection review, dataset curation, labeling schema design, computer vision annotation, QA evaluation, and iterative training pipelines. You will contribute to model evaluation reports, maintain reliable test sets, and support deployment readiness checks so perception improvements translate to safer, more accurate robot behavior.

Remote Work And Collaboration

This is a Remote, FULL_TIME role based in the US. You will collaborate asynchronously with distributed engineering teams, using documented experiment tracking, structured code reviews, and repeatable evaluation protocols. Remote roles must stay explicitly Remote, and this position remains Remote.

Compensation

Salary range is 63360 to 126720 USD per YEAR, depending on experience and scope. Compensation may align with market benchmarks for computer vision robotics engineering and the specific needs of agricultural robotics teams.

How To Apply On Rex.zone

Apply through Rex.zone by submitting your resume and a short summary of relevant agricultural robotics, computer vision, and dataset work. Include links to projects that demonstrate perception modeling, evaluation, dataset curation, or edge deployment experience.

Frequently Asked Questions

Q: What is the main focus of this Agricultural Robotics Computer Vision Engineer role?
The focus is building and improving perception for agricultural robots using computer vision, with strong emphasis on dataset curation, computer vision annotation strategy, training data quality, and model evaluation that leads to measurable field performance improvement.
Q: Is this role remote and full-time?
Yes. Remote Type is Remote and Employment Type is FULL_TIME, aligned with the job metadata.
Q: What kinds of computer vision tasks are common in agricultural robotics?
Common tasks include crop/weed detection, fruit counting, instance segmentation for plants, row following, obstacle detection, and robustness to domain shift caused by lighting, weather, occlusion, and seasonal variation.
Q: How important is data labeling and annotation quality for this job?
It is central. You will define labeling schemas, write annotation guidelines, verify annotation guidelines compliance, and run QA evaluation to ensure training data quality and reliable ground truth for model training pipelines.
Q: Does this role involve robotics stack integration?
Yes. You will coordinate perception outputs with robotics engineers and support integration considerations such as ROS2 messaging, sensor fusion, and edge deployment constraints.
Q: What experience level is targeted?
Experience Level is Mid-Senior, suitable for candidates who have shipped or significantly contributed to production-like computer vision or robotics perception systems.
Q: What are the key skills for SEO-aligned fit?
Computer Vision, Robotics Perception, Image Segmentation, Object Detection, Visual SLAM, Sensor Fusion, ROS2, Edge Deployment, Dataset Curation, and Data Labeling.
Q: What salary range is offered and in what currency?
Salary Currency is USD, with a YEAR pay period and a range of 63360 to 126720.

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