R&D Engineer – Machine Learning & Image Processing
An R&D engineer experienced in Machine Learning & Image Processing technologies, is being sought by Spectral Edge.
We are looking for an individual with knowledge of Machine Learning techniques, specifically in the field of Image Processing. This individual should be experienced in Object Detection techniques (DarkNet, Yolo, SSD) and Neural Network frameworks (Tensorflow, Caffe). The core of the role is to apply Machine Learning techniques to solve various Image Processing problems, 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 problems set out by the CTO
• Specifically, applying ML to the areas of interpolation, registration and tone mapping
• Work with the software team and the hardware team to 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 or Image Processing algorithms and Image Quality procedures & criteria is essential
• Strong mathematical background. Masters degree or above in Maths or Computer Science expected
• Good communication and team-working skills
• Some industrial experience preferred
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 be 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.
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