Computer Vision Engineer (All levels)
Role overview
Join our world-class computer vision team as we revolutionize horticulture automation. Whether you're a recent graduate eager to make your mark or an experienced engineer looking for your next challenge, you'll develop cutting-edge perception systems that enable robots to understand and interact with complex greenhouse environments - from identifying ripe produce to detecting plant diseases and optimizing crop health.
As a Computer Vision Engineer at Eternal, you'll be part of a high-performance culture that values first-principles thinking and rapid iteration. You'll work at the intersection of classical computer vision and modern deep learning, creating perception systems that operate reliably in challenging agricultural environments with varying lighting, occlusions, and organic variability.
You'll collaborate with a distributed team across our Cologne HQ and Bengaluru office, pushing the boundaries of what's possible in agricultural computer vision while delivering practical solutions that work 24/7 in production environments.
Responsibilities
- check_circle Design and implement robust computer vision algorithms for crop detection, ripeness assessment, and precise localization in dynamic greenhouse environments
- check_circle Develop deep learning models for multi-class segmentation, object detection, and tracking of plants, fruits, and agricultural structures
- check_circle Create real-time perception pipelines that process 2D/3D sensor data for robotic decision-making with sub-centimeter accuracy
- check_circle Build intelligent systems that adapt to varying environmental conditions, including changes in lighting, plant growth stages, and seasonal variations
- check_circle Optimize vision algorithms for edge deployment on robotic platforms, balancing accuracy with computational efficiency
- check_circle Implement continuous learning systems that improve model performance through data collected from our deployed robot fleet
- check_circle Collaborate cross-functionally with robotics engineers, AI/ML researchers, and crop scientists to deliver end-to-end perception solutions
- check_circle Bachelor's degree in Computer Science, Electrical Engineering, Applied Mathematics, or related field (or graduating by Summer 2025)
- check_circle Strong programming skills in C++ and/or Python for computer vision applications
- check_circle Understanding of fundamental computer vision concepts: image processing, feature detection, camera calibration, and 3D geometry
- check_circle Experience with deep learning frameworks (PyTorch, TensorFlow) and classical CV libraries (OpenCV)
- check_circle Familiarity with Linux environments and version control systems
- check_circle Passion for solving complex real-world problems with tangible impact
- check_circle Recent graduate or final year student with strong academic performance
- check_circle Hands-on computer vision experience through internships, research projects, or competitions
- check_circle Demonstrated programming skills through coursework or personal projects
- check_circle Understanding of CNNs and basic deep learning architectures
- check_circle Solid foundation in both classical and deep learning-based computer vision
- check_circle Experience deploying at least one vision system from research to production
- check_circle Proficiency with modern architectures (YOLO, Mask R-CNN, Vision Transformers)
- check_circle Understanding of model optimization techniques and edge deployment
- check_circle Proven track record of deploying vision systems in production environments
- check_circle Experience with 3D vision, multi-sensor fusion, or SLAM algorithms
- check_circle Knowledge of model optimization for embedded systems (quantization, pruning, distillation)
- check_circle Ability to mentor junior engineers and lead technical initiatives
- check_circle Technical leadership experience with complex perception systems
- check_circle Deep expertise across multiple vision domains (2D/3D, classical/learning-based)
- check_circle Strategic thinking about perception architecture and technology roadmaps
- check_circle Track record of building and scaling high-performance computer vision teams
Preferred qualifications
- Experience with agricultural or outdoor computer vision applications
- Knowledge of 3D sensors (stereo cameras, LiDAR, structured light)
- GPU programming skills (CUDA) for accelerating vision algorithms
- Experience with vision-language models or foundation models
- Familiarity with ROS2 for perception system integration
- Publications at top-tier computer vision conferences (CVPR, ICCV, ECCV)
- Open source contributions to computer vision projects
- Vision Libraries: OpenCV, PCL, Open3D
- Deep Learning: PyTorch, TensorFlow, ONNX
- Deployment: TensorRT, OpenVINO, ONNX Runtime
- Sensors: RGB cameras, stereo vision, depth sensors
- Infrastructure: Cloud-native training pipelines, edge deployment systems
- Integration: ROS2 for robotic system integration