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# AI & Computer Vision Engineer

- **Company:** Brightlands
- **Location:** Venlo, Netherlands
- **Salary:** €1,700.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Computer Vision, Object Detection, Pytorch, Deep Learning, Information Technology, ONNX (Open Neural Network Exchange) Format, Hardware Acceleration, TensorRT
- **Published:** August 11, 2026
- **Apply:** https://www.werk.nl/nl/vacatures/70788239

## About the Role

* Bachelor's or Master's in Computer Science, AI, Robotics, or related field.
 * Strong hands-on experience with PyTorch and deep learning model development.
 * Background in computer vision tasks: detection, segmentation,...

## Description

AI & Computer Vision Engineer Luxeed Kruimelpad - AI & Computer Vision Engineer
LUXEED Robotics B.V. is a Dutch early-stage AgTech startup that uses lasers and AI to burn weeds with high
precision, ZERO herbicides needed. We're making lasers more affordable and accessible to farmers to help them
transition to organic and regenerative weeding. LUXEED has received funding and recognition from various national
and international players. Please watch the following video to know more about the robot. Do you want to be part of a
cutting-edge technology that will eliminate the use of herbicides by zapping weeds with lasers?
This is a remote and task-based opportunity where your problem-solving and critical skills, communication, and ability
to adapt will be highly challenged. We expect around 40 hrs/week. Compensation Starting at € 1,700/month.
Responsibilities

 * Design, train, and evaluate deep learning models in PyTorch for tasks such as object detection,
segmentation, regression, and keypoint tracking.

 * Work with state-of-the-art architectures and adapt them to our agricultural use cases.
 * Optimize models for embedded deployment using TensorRT, ONNX, and hardware accelerators.
 * Build efficient training and inference pipelines, with an emphasis on speed, accuracy, and robustness.
 * Conduct experiments, manage datasets, and implement preprocessing/augmentation strategies to improve
model generalization.

 * Collaborate with robotics and software teams to integrate models into real-time robotic control systems.
 * Track metrics and performance in production, iterating to improve reliability in field conditions.


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