• Establishment of the software and compute infrastructure for AI and computer vision projects including training, model serving and MLOps.
  • Design and training of AI systems – we routinely apply, but also innovate on, the latest developments in data augmentation, deep-learning architectures, loss functions, and training and self-supervision strategies.
  • Data processing and analysis – we effectively address various issues with data including small datasets, noisy ground-truth labels, and time or memory limits for data processing.
  • Explainable AI – we provide data visualizations aimed at explaining the inner-workings of our AI models, such as attention maps, highlighting parts of the data that are important for prediction


  • Experience with deep learning frameworks such as PyTorch and TensorFlow and MLOps tools such as MLflow and DVC
  • Google Cloud, AWS, Azure
  • Expertise using Python, C++ and latest web technologies