As ML algorithms team, we are responsible to source our training data by specifying requirements and ensuring their implementation in both real and synthetic data campaigns. Proficiency in data handling, including capturing, processing and managing large datasets, data cleaning, transformation, and augmentation, is essential to tailor the data to specific model and use case needs. Identifying the ideal algorithm and model architecture for a particular use case and hardware configuration demands a comprehensive knowledge of available options. Therefore, it is a valuable asset to have experience with a range of deep learning techniques, such as traditional CNNs, transformers and, for instance, neural rendering or diffusion methods. Numerous data-driven decisions must be made, necessitating expertise in model evaluation using traditional or custom KPI metrics as well as computational efficiency on large GPU clusters (training) and on-device SOCs (deployment). Building image-based 3D reconstructions requires a strong understanding of 3D computer vision and image formation principles, as well as mathematics, including linear algebra and optimization. Experience with 3D data representations such as e.g. meshes, point clouds, depth textures, or voxels is advantageous for handling complex 3D data.In this role effective communication is essential for conveying requirements, challenges, and solutions to a diverse audience, both technical and non-technical, within cross-functional teams. Strong communication skills and a collaborative mindset are a must.