R&D Engineer – Machine Learning & Image Processing REF:RD004
We are growing fast and are looking for an R&D engineer experienced in Machine Learning & Image Processing technologies.
You should have knowledge of Machine Learning techniques in the field of Image Processing, with experience in Object Detection techniques (DarkNet, Yolo, SSD) and Neural Network frameworks (Tensorflow, Caffe). The core of this exciting role is to apply Machine Learning techniques to Image Processing problems as required, by defining the architecture, preparing the training set, training the system and evaluating the results.
The key duties are:
• Applying machine learning techniques as appropriate to various problems including to the areas of interpolation, registration and tone mapping
• In conjunction with our software and hardware teams, deploy the processing units based on machine learning as hardware or software IPs
The key skills are:
• Strong Python, C/C++ skills and a working knowledge of Matlab
• Knowledge of Image Processing algorithms and Image Quality procedures & criteria
• Strong mathematical background. Master’s degree or above in Maths or Computer Science expected
• Good communication and team-working skills
The following additional skills would be an advantage:
• Experience of GPUs and languages such as OpenGL/CL, CUDA
• Driver development for cameras and sensors on Linux platforms
• Knowledge of ISP block algorithms, e.g de-noising or image registration algorithms
Candidates should have a good command of written English and be able to work full-time at our offices in Cambridge, UK.
As well as a competitive salary, the successful candidate will be awarded stock options in an early-stage start up with considerable growth potential. This is a demanding technical role with a career path to more senior role as the business develops.
For further information or to apply, please contact our HR Manager, Sue Handley Jones email@example.com
Spectral Edge is a specialist in Image Fusion, using patented algorithms known as “Phusion” to combine multi-spectral images or video into single images/videos in an aesthetically pleasing way. Applications include:
• Computational Photography on smartphones
• Video enhancement for content delivery, including assistive technology