The clients are developing cutting-edge technology to edit and translate video content. They build AI technology that understands and models the intricacies of the human face, bringing entirely new capabilities to content creators.
Located in Angel, London they are a team of researchers from UCL, Stanford and Cambridge with experience in deep learning, visual effects and computer vision. They are passionate about making it easier to translate video content and make great stories travel the world.
The clients technology revolves around modelling, understanding and synthesizing people in a way that is indistinguishable from normal video. They are looking for someone to take a key role in developing their deep learning capabilities for visual analysis and synthesis in video. You will be undertaking cutting edge research working with a world class team of researchers in direct collaboration with internationally leading academics in the field.
You will need a background in deep learning for image and video processing, and have an inquisitive nature with a clear track record experimenting with state-of-the-art techniques. You have strong experience in Python and deep learning frameworks (Tensorflow, PyTorch) as well as a keen interest in bringing these technologies to a production level cloud platform (AWS), solving real-world challenges backed by best-in-class data-sets.
This is an opportunity to join a proven start-up as one of the first 15 employees as the company scales. They are looking for someone that is passionate about solving complex problems, can take a creative approach to delivering requirements in a fast-paced environment and enjoys working in a highly collaborative team bringing cutting-edge technologies to market.
- PhD involving deep learning for image and video processing.
- Strong experience in Python and deep-learning frameworks (Tensorflow, PyTorch).
- Track record in implementing state-of-the-art techniques in deep learning.
- Passionate about developing cutting-edge research and working in a startup.
- An advanced degree in a quantitative discipline (e.g. Applied Mathematics, Physics, Engineering).
- Experience with or knowledge of procurement, supply chain or invoicing data.
- Working knowledge of common database technologies (e.g. SQL)
- Experience of using machine learning in a financial services context or in the context of supply-chain optimisation.
- Comfortable with noisy, biased or incomplete datasets.
- Private Pension