Machine Learning Engineer
Are you an experienced machine learning / artificial intelligence practitioner? Joining the Electronics UK business side of a world leading Aerospace and Defence company.. The successful candidates will take a prominent role in delivering world leading sensor solutions across a variety of military platforms in a collaborative, agile environment.
You role will be to lead the implementation of AI/ML technologies into specialist software products, these products are a major contributor to aircraft survivability and enhancing future mission effectiveness.
What you will do
- You will lead the scoping, implementation and strategic planning of AI/ML technologies for incorporation into different sensor products.
- You will identify opportunities within complex data sets and leverage industry leading COTS technologies to deliver advanced analytic capabilities to the forefront of Aircraft defensive systems.
- You will support the development of early careers engineers leveraging your experience and skills to create a core AI/ML capability within the business.
What we are looking for
- People with experience of and a passion for developing machine learning and artificial intelligence solutions.
- You should have a degree level qualification (or equivalent) in mathematics, physics, computer science, engineering or a related discipline and a desire to learn and share knowledge with others.
- Due to the nature of the tasks involved, you must be able to satisfy the necessary security clearance for the role and meet a minimum of 5 years permanent residency in the UK.
In addition you will be expected to have some of the following skills:
- Strong numerical, analytic and problem solving skills for algorithm development.
- Signal processing, image processing, data processing, high performance computing; or scientific or mathematical libraries for numerical computing, simulation or optimisation problems.
- Programming in Python, Matlab or C/C++.
- Machine learning frameworks such as Pytorch, Keras/Tensorflow.
- Matlab Deep Learning framework.
- Common neural network architectures.
- Embedded systems, edge computing or cloud computing.
- High performance computing hardware including parallel computing/multi-processing, GPU acceleration.
- Software development lifecycle and Dev Ops.
- Real-time systems with sensors, internet of things or imaging applications (infrared, Lidar, Radar, radio frequency).