Computer Vision Engineer (All levels)

• Recent graduate or final year student with strong academic performance
• Hands-on computer vision and/or deep learning experience through internships, research projects, or competitions (Kaggle, university labs, personal projects)
• Demonstrated programming skills through coursework or personal projects
• Understands model training and evaluation basics, including common failure modes

• Has built a training pipeline end-to-end: data collection/curation, training, evaluation, deployment, and iteration based on real-world feedback
• Experience taking at least one vision or ML system from prototype to production
• Proficiency with modern architectures (YOLO, Mask R-CNN, Vision Transformers)
• Can make sound tradeoffs between classical and learning-based components in a perception pipeline
• Practical experience with model optimization for edge deployment (quantization, distillation, TensorRT/ONNX export)

• Proven track record of deploying production perception systems and making sound architectural tradeoffs between classical and learning-based approaches
• Experience designing data strategies: what to collect, how to annotate efficiently, when to retrain vs. fine-tune
• MLOps experience: automated retraining pipelines, model versioning, drift detection, or A/B evaluation across a deployed fleet
• Knowledge of model optimization for embedded systems (quantization, pruning, distillation)
• Ability to mentor junior engineers and make technical decisions that compound over time

• Technical leadership experience with complex perception systems at scale
• Has built and iterated on the full ML lifecycle: training infrastructure, data flywheels, MLOps, model monitoring, continuous improvement at fleet scale
• Strategic thinking about perception architecture, including when learned methods outperform engineered solutions and when simpler methods remain preferable
• Track record of building and scaling high-performance computer vision teams

• Experience with foundation models, vision-language models, or transfer learning for domain adaptation
• Knowledge of 3D sensors (stereo cameras, depth sensors)
• Familiarity with ROS2 for perception system integration
• Experience with MLOps and ML infrastructure: experiment tracking, model versioning, automated retraining, data management at scale
• Publications at top-tier computer vision or ML conferences (CVPR, ICCV, NeurIPS, ICML)
• Open source contributions to computer vision or ML projects

Launch Your Career: For new graduates, this is a unique opportunity to join a proven team and learn from engineers who've already built and deployed commercial vision systems. You'll get hands-on experience with cutting-edge technology while making a real-world impact from day one.

Impact at Scale: Your perception algorithms will directly enable robots to transform 800,000 hectares of greenhouses worldwide into sustainable, autonomous food production facilities.

Technical Excellence: Work with state-of-the-art computer vision technology including modern deep learning architectures, 3D perception, and multi-modal sensor fusion.

Rapid Innovation: Our software-first approach means you'll see your models deployed to real robots in hours/days, not months. We've proven we can develop and deploy new perception capabilities over-the-air as crops evolve.

Unique Challenges: Tackle perception problems that combine the complexity of outdoor vision (varying lighting, weather) with the precision requirements of industrial automation.

Growth Opportunity: Join as we scale from proof-of-concept to global deployment. Be part of the core team shaping the future of agricultural perception. Clear career progression from graduate to senior engineer and beyond.

Mentorship & Learning: Work alongside experienced computer vision engineers who've solved complex real-world problems. We invest in your growth through hands-on projects, technical mentorship, and exposure to all aspects of vision system development.

Flexible Work Culture: Distributed team with offices in Cologne and Bengaluru, following a "follow-the-sun" support model for our 24/7 operations.

• Deep Learning: PyTorch, ONNX
• Deployment: TensorRT, ONNX Runtime
• Vision & 3D: OpenCV
• Sensors: RGB cameras, stereo vision, depth sensors
• Data & Training: Cloud-native training pipelines, experiment tracking, annotation tooling
• Infrastructure: Edge deployment systems, OTA model updates
• Integration: ROS2 for robotic system integration

Ready to revolutionize how the world grows food through advanced computer vision? Whether you're starting your career or looking to make a bigger impact, join us in building perception systems that will enable sustainable food production for billions.

We're committed to building a diverse and inclusive team. We encourage applications from candidates of all backgrounds and experience levels who are excited about our mission and show potential to grow with us. Recent graduates - don't let experience requirements hold you back; we value passion, potential, and fresh perspectives.

eternal.ag is building fully automated food production sites that can sustainably produce fresh food year-round. Backed by world-class investors and partnering with leading agricultural companies, we're turning the vision of fully-autonomous greenhouses into reality.