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Hub is seeking a Computer Vision Engineer to advance our image-analysis models on an existing production platform. You will train, evaluate, and optimize deep learning models for real-world waste imagery, going beyond research to deliver robust production solutions.
You will work with datasets, apply augmentation, and iterate on architectures to improve accuracy, speed, and reliability in real-world settings. Strong Python, PyTorch or TensorFlow, and CV knowledge are essential.
We are looking for a Computer Vision Engineer to develop and improve our image-analysis models on an existing platform that is already in production.
You will train, evaluate, and optimize deep learning models for real-world waste imagery, not research in isolation.
Develop, train, and optimize deep learning models for image analysis
Evaluate and extend existing model architectures
Improve accuracy, performance, and robustness
Work with image datasets: processing, augmentation, and evaluation
Experiment with architectures and training approaches
Solve hard technical problems and iterate on production CV systems
At least3 years of hands-on computer vision and model development, orequivalent documented experience(professional work, open source, or serious personal projects)
Experience building, training, evaluating, and optimizing DL models for images
Solid understanding of CV architectures (e.g. CNNs, Vision Transformers)
StrongPython
PyTorch or TensorFlowin practice
Real-world image datasets: preprocessing, augmentation, evaluation
Ability to adapt and extend existing models against clear technical requirements
We care aboutwhat you've built, not only job titles. Show projects, repos, or results you can point to.
Edge AI and running models on edge or embedded devices
Optimization for limited compute (quantization, inference performance)
Real-time image or video analysis
Curious, solution-oriented, and happy to go deep on technical problems. You keep up with CV and DL, turn ideas into improvements, work independently and in a structured way, and collaborate well on technical decisions.
Production computer vision with real impact
A platform that already runs. You improve what ships.
Room to influence architecture and methods
Hard problems where precision, robustness, and performance matter