ZEG.ai is a london-based, venture-backed startup working on fully automated generation of 3D content using deep learning and computer vision. We are building a unique technology from the ground up based on our expertise in machine learning, computer vision and augmented reality.
Our technology anticipates a massive growth in the demand for 3D content as we move away from 2D screens into immersive experiences in AR and VR, with already established commercial applications in e-commerce and gaming. ZEG.ai is funded by well-known, serial entrepreneurs with impressive backgrounds across artificial intelligence, blockchain, e-commerce, and augmented reality.
As a Deep Learning Engineer, you will be working with top scientists to develop state-of-the-art hybrid architectures and data pipelines for a range of 3D deep learning problems, including segmentation, reconstruction, texture synthesis, denoising, rendering, style transfer, object detection and information fusion. This is a great opportunity to join an exciting company at an early stage and create a deep impact across multiple industries. You will get to work with top scientists from Imperial/ UCL/ INRIA to push technology further.
- Experience in designing custom deep learning models.
- Proficiency in Python or C/C++, knowledge of the fundamentals of convolutional networks and experience working with at least one common architecture (GANs, ResNet, DenseNet, etc).
- Proficiency with at least one of the general purpose deep learning frameworks (e.g., Keras, TensorFlow, Theanos, Caffe), comfortable with learning new frameworks.
- Should be excited about early-stage startups and 3D; entrepreneurial, highly driven, willingness to challenge yourself and grow, comfortable with risk & uncertainty.
- Experience working with 3D meshes, such as problems in object reconstruction, global parameterization (uv mapping), and establishing correspondences.
- MSc or PhD in Computer Science or Applied Mathematics, ideally with a focus on deep learning, computer vision, machine learning and/or computational geometry
- Experience in developing architectures for reinforcement learning.
- Peer-reviewed publications in venues such as: CVPR, ICCV, ECCV, ICLR, NIPS, PAMI
- Play a foundational role at a well-funded startup with a ground-breaking 3D technology.
- Join a driven, talented and passionate team tackling some of the most challenging and rewarding problems in computer vision with real commercial applications.