Deep learning algorythm researcher
About The Position
Zebra medical vision is a leader in modern computer vision and deep learning practices.
The Algorithms team is in charge of creating cutting edge algorithmic products which detect and diagnose a wide variety of medical conditions from medical images.
These solutions help make medical diagnosis faster, more accurate and more accessible to millions of patients around the world.
As a researcher on the team, you will get a chance to work alongside some of the best minds in the field and implement the latest Deep learning and computer vision algorithms, in a multidisciplinary and dynamic environment.
Research, design and develop deep learning and computer vision algorithms to detect a wide variety of medical conditions from image and textual data.
Work with a multi-disciplinary team of medical doctors, engineers, and designers to deliver an end-to-end product: from the idea phase, through collecting and assessing the data, exploring algorithmic approaches, developing, testing, validating and integrating the algorithm in the production environment.
Read and implement algorithms publications in the field of deep learning and computer vision, as well as publish your own work, and contribute to the community via conferences/meetups etc.
MSC. in computer science, preferably in the field of machine learning/computer vision
3+ years of hands on development of complex machine learning models using modern frameworks and tools (ideally python based)
1+ year of hands on experience with Deep learning using common open source frameworks and tools (Keras, TensorFlow, Theano, Caffe etc.)
Strong communication and collaboration skills
Team player, positive and driven, fast learner
PHD in machine learning - very strong advantage
Experience in computer vision - very strong advantage
Publications/public speaking experience in the field of Machine learning - strong advantage
Experience in engineering, especially working with CI, docker, Hadoop, and distributed systems - strong advantage