Computer Vision Research Engineer
Magic Pony Technology Ltd, London, UK
£40-70k p.a. plus equity
Contact: Dr. Zehan Wang, email@example.com
Magic Pony Technology is seeking an ambitious computer vision research engineer to join our team of computer vision and machine learning experts working on state-of-the-art image and video analysis technologies. This is a rare opportunity to join a well-funded, early-stage company at a point where your decisions and contributions will have a key impact on our growth and potential.
We’ve recently closed a seven figure seed funding round (backed by some of the most prestigious venture capital investors in Europe) and are looking to expand our core R&D team. In the last month, we’ve been featured in The Times and had one of the founding team members listed by the Financial Times as one of the Top 10 Technology Entrepreneurs under 30 in Europe.
Our ideal candidate will have strong academic experience developing novel computer vision and machine learning techniques and applying them to solve real-world problems.
In particular, hands-on experience with using patch-based image enhancement techniques as well as image analysis and classification approaches, particularly using deep learning techniques, would be highly desirable. In addition, we also look for strong software engineering skills and the ability to work well within a close-knit team with diverse backgrounds.
- PhD with specialism in computer vision and machine learning
- International publications in relevant fields in top-tier conferences and journals, such as CVPR, ICCV, ECCV, ICML, MICCAI, NIPS, PAMI, IJCV, etc
- Strong mathematical understanding of signal processing algorithms
- Up-to-date knowledge and understanding of recent advances in machine learning, particularly deep learning
- Strong experience in image processing techniques, particularly for image analysis and enhancement
- Strong experience in software engineering, with a good understanding of software architecture, optimisation, and programming patterns and paradigms.
- Experienced with prototyping computer vision algorithms in Python and using the Numpy/Scipy stack
- Experienced with using and modifying deep learning frameworks such as Caffe and Theano
- Experienced with modern C/C++
- Familiarity with CUDA and OpenCL