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# Computer Vision Engineer

- **Company:** Clera
- **Location:** München, Germany (Remote available)
- **Experience:** Experienced
- **Salary:** €65,000.0 - €95,000.0
- **Contract:** Permanent contract
- **Skills:** 3D Scanning, Computer Vision, C++ (Programming Language), Nvidia CUDA, Python (Programming Language), Object Detection, OpenCV, Tensorflow, Visual Systems, Pytorch, Deep Learning, Lidar
- **Published:** May 26, 2026
- **Apply:** https://de.indeed.com/viewjob?jk=bd7c1069447670b0

## About the Role

* 3+ years of hands-on experience with real-time computer vision pipelines for 3D perception and surface analysis
 * Experience with depth cameras, LiDAR, or laser scanners and processing of 3D point clouds
 * Proficiency in Python and/or C++ for robotics or vision applications
 * Hands-on experience with OpenCV and PCL (Point Cloud Library) in production perception systems
 * Experience with deep learning frameworks (PyTorch or TensorFlow) for detection, segmentation, pose estimation, and defect classification
 * Experience with CUDA-enabled acceleration and optimization for embedded/edge targets
 * Ability to calibrate and maintain multi-sensor perception stacks
 * Experience translating visual outputs into robot control signals and contributing to sensor architecture decisions

Nice to Have:

 * Familiarity with ROS 2 for robotics integration
 * Background in industrial inspection or quality-control vision systems

## Description

Join an early-stage industrial robotics startup building a fully flexible, intelligent robotic work cell for surface finishing, welding, and coating. As a foundational perception engineer, you will be the person who gives the robot its eyes - designing and owning the real-time vision pipelines, 3D perception systems, and deep learning models that make autonomous industrial automation possible. This is a high-ownership, high-impact role at the core of the product., * Build and maintain real-time computer vision pipelines for surface analysis and defect detection

 * Develop 3D perception systems using depth cameras, laser scanners, and/or LiDAR, including point cloud processing
 * Own calibration and maintenance of the full perception sensor stack (camera/LiDAR calibration workflows)
 * Train and deploy deep learning models for object detection, pose estimation, segmentation, and defect classification
 * Optimize model inference for edge and embedded deployment using CUDA-enabled acceleration
 * Translate visual outputs into actionable robot control signals
 * Define, architect, and iterate on the sensor stack as the product evolves


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