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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Computer Vision Engineer - **Company:** Twentyfour GmbH - **Location:** München, Germany (Remote available) - **Experience:** Experienced - **Contract:** Permanent contract - **Skills:** Clean Code Principles, Computer Vision, Continuous Integration, Data Files, Machine Learning, Object Detection, Software Engineering, Software Requirements Analysis, Management of Software Versions, Pytorch, Deep Learning, Model Validation, Data Strategy, Information Technology, ONNX (Open Neural Network Exchange) Format, Machine Learning Operations, TensorRT - **Published:** August 1, 2026 - **Apply:** https://www.adzuna.de/details/5823035619 ## About the Role * Computer vision depth: you have a deep understanding of CNN- and transformer-based architectures, training dynamics, and evaluation, with hands-on PyTorch experience * Dataset discipline: you're experienced building representative dataset splits, validating annotations, and conducting detailed failure analysis * MLOps experience: you have end-to-end production MLOps ownership, including experiment tracking, versioning, and CI/CD * Software engineering: you write clean, tested, maintainable code and make sound performance and algorithmic trade-offs * Track record: you bring 3+ years building and shipping computer-vision or ML systems with ownership across data, training, and evaluation * Education: you hold a degree in computer science, machine learning, or a related field - or equivalent hands-on experience that speaks louder than the diplom Nice to haves * Aerial vision: experience with drone imagery, small-object detection, or low-light and low-resolution data * Simulation expertise: hands-on experience with NVIDIA Isaac Sim, Omniverse, or other synthetic-data workflows * Inference optimization: experience exporting and optimizing models with ONNX and TensorRT on NVIDIA Jetson hardware ## Description We're looking for a highly driven Computer Vision / ML Engineer to own the data, training, evaluation, and MLOps lifecycle for our camera-based perception models. In this role, you'll turn real and synthetic drone data into reproducible, validated model releases, building and operating the pipelines that connect collection, annotation, training, evaluation, and release. This is an ownership role where you'll work closely with robotics perception engineers, directly shaping how reliably our systems see and understand the world. The day-to-day * Requirements definition: define clear objectives, interfaces, datasets, and release standards for deep learning tasks such as detection, segmentation, and depth * Data strategy: drive real-world and synthetic data collection campaigns covering edge cases, rare conditions, and class imbalance * Dataset integrity: build trusted, versioned, auditable datasets, maintaining lineage and preventing leakage across every model release * Model performance: develop, train, and benchmark detection, segmentation, and depth-estimation models for aerial imagery * Evaluation rigor: set a high bar for evaluation and failure analysis across range, altitude, illumination, and weather conditions * MLOps ownership: build and operate the end-to-end pipeline for versioning, reproducible training, experiment tracking, and CI/CD * Deliver production-ready model artifacts. 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