Machine Learning Engineer
Dynamic British Tech for Good company expanding with private investment!
£40,000 - £50,000 + benefits (negotiable)
Hybrid working / flexible - London HQ
Exclusive role available for exceptional and ambitious machine learning engineers. You won't find this opportunity anywhere but through Made's community, apply now.
The gig:
If you are a Machine Learning Engineer and looking to work for a firm on the cutting edge of research and development within a product-deployed environment, delivering state-of-the-art tech for good solutions and segmentation in relation to AI-based computer vision security - then read on!
This client is not only a leader in its field, it's received significant private investment and interest across the British tech scene. Run by some of the brightest minds in AI vision security, this truly is an opportunity to propel your career.
What cool stuff you will be involved in:
- Design and implement computer vision models for objection detection, segmentation, and tracking.
- Stay up to date with latest developments in ML and rapidly prototype new advances in computer vision research.
- Provide input into the direction of the business and future product road maps.
- Develop, support, and maintain the pipelines that underlie ML technologies.
Experience / Skills you need to have:
- Masters or PhD in a relevant field, ideally with a publication record.
- Excellent theoretical grounding in ML concepts and algorithms.
- Experience with a broad range of supervised and unsupervised methods, e.g. random forests, support vector machines, linear/logistic regression, dimensionality reduction, clustering, ensemble methods, bootstrapping, maximum likelihood estimation, and Bayesian methods.
- Experience training a range of deep learning architectures ( e.g. multilayer perceptrons, recurrent networks, and convolutional networks).
- Experience developing cutting-edge computer vision models for image classification and object detection (e.g. ResNets, RCNNs).
- Experience implementing, adapting, and optimising published state of the art deep learning models.
- Fluency in Python.
- Proficiency using Python numerical packages (e.g. NumPy, SciPy, scikit-learn).
- Proficiency using Python deep learning framework (e.g. TensorFlow, PyTorch).
- Experience with software engineering practices, such as using version control and continuous integration.