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Senior Computer Vision and AI Engineer Lead

Germany
Full Remote
This Job is No Longer Active This position is no longer accepting applications

Lead the development and deployment of advanced vision-based perception systems for autonomous inspection robots, leveraging computer vision and AI expertise, and utilizing deep learning and machine vision techniques.

Key Highlights
Develop and deploy vision-based perception algorithms using deep learning and machine vision techniques
Design and implement supervised, self-supervised, and unsupervised learning methods for multi-modal data
Integrate perception pipelines into robotic systems using ROS1/ROS2
Key Responsibilities
Develop and deploy vision-based perception algorithms using deep learning and machine vision techniques
Design and implement supervised, self-supervised, and unsupervised learning methods for multi-modal data
Build and optimise models for 2D & 3D object detection and tracking, anomaly detection, LiDAR-based semantic segmentation and 3D point cloud understanding
Optimise models for edge deployment and deploy on NVIDIA Jetson platforms
Integrate perception pipelines into robotic systems using ROS1/ROS2
Technical Skills Required
PyTorch OpenCV MMDetection Detectron2 TensorRT ONNX Docker NVIDIA DeepStream ROS1 ROS2 AWS SageMaker Bedrock GitLab CI/CD MLflow n8n CVAT Label Studio 3D Point Labeler
Benefits & Perks
Fully remote

Job Description

Senior Computer Vision and AI Engineer

About the job

Edison Smart are searching for a Senior Computer Vision & AI Engineer to lead the development and deployment of advanced vision-based perception systems for autonomous inspection robots. This is a hands-on role focused on building, optimising, and deploying deep learning models across multi-modal sensor stacks in real-world robotic environments.

This is a fully remote contract opportunity, offering €80–100 per hour, for an initial 6-month engagement.

Key Responsibilities

Develop and deploy vision-based perception algorithms using deep learning and machine vision techniques (CNNs, ViTs, DETR-based models)

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