Leonardo

Systems Engineer - Machine Learning

Ref No. BHN538809
Location Bristol, England
Job type Permanent
Job Status Closed
You can not apply for this job as its status is Closed.
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Introduction

Begin your career with Leonardo! A fantastic opportunity for a Machine Learning Engineer to join us on the most exciting projects in the defense sector.

Leonardo

Important

Position subject to security clearance and proof of 5 - year UK residency

The Job

Leonardo has a good opportunity for a Machine Learning Engineer within the Combat Air business group, on a project developing a new generation of airborne electronic warfare (EW) detection and countermeasure system for a future multi-role combat aircraft. The candidate will be part of a multi-disciplinary team, working with other mathematicians, but also software and systems engineers.

The candidate will evaluate and deploy Machine Learning techniques to solve problems in the areas of signal processing and data fusion. The algorithms developed by the candidate will be pivotal in achieving the required capabilities and performance of the equipment.

What you will get:

  • Advanced Technology: You will have the ability to contribute to the development of market-leading product development, providing advanced capability and protection to UK platforms.
  • Agile Systems Engineering: Apply and develop your machine learning skills as part of systems engineering in a fast-paced and dynamic agile development team.
  • EW Expertise: Leonardo has subject-matter experts with extensive and detailed knowledge of the EW domain, technology, performance, and operational capabilities
  • Continuous Learning: Whether it's EW, engineering lifecycle processes, leadership, or personal development, you'll be supported in your ongoing professional development though training and mentoring.
  • Teamwork: You will work alongside like-minded professional engineers in a highly collaborative environment.
  • Connections: You will be joining an expansive team of around 7,000 people at six major sites across the UK.
  • EW Heritage: We are proud of our rich heritage in EW, with 100 years of pioneering EW development. Today we're providing the latest advances in sensor technology and enabling our customers to keep pace with an ever-evolving EW environment.

What you will do:

  • Assess next-generation airborne EW sensor / effector system problems, with a view to utilising machine learning techniques.
  • Work with colleagues to understand and enhance the data environment of the project, with a view to ensuring that machine learning algorithms can be robustly evaluated.
  • Utilise a variety of modelling tools (such as Mathworks, Python SDKs or other off the shelf apps), and implementing algorithms onto target equipment.
  • Analysis of data recorded on flight-trials and/or synthesised by simulation.
  • Technical reporting of key findings and recommendations.

What we are looking for:

  • Strong applied mathematical skills with demonstrable Machine Learning knowledge.
  • Be skilled in the use of machine learning tools such as MATLAB and PyTorch
    • Ability to code neural network layers, e.g. full, convolutional...
    • Ability to implement and explain back propagation.
  • Experience of the challenges associated with obtaining good datasets for machine learning problems, together with a pragmatic view on how to address those challenges.
  • Self-motivated with an aptitude for problem-solving and driving difficult issues to conclusion.
  • Sound verbal and written communication / collaboration skills.
  • Knowledge of underlying Radio Frequency (RF) physics and receiver processing chains.

It would be nice if you had:

  • Knowledge of the theory of Artificial Intelligence/Machine Learning techniques. For example, how RNNs function and optimisation algorithms can be compared.
  • Experience of coding (e.g. C++).
  • Understanding of passive / active EW detection and countermeasure systems; technology, performance, operation, and use.
  • Knowledge of airborne and ground-based radar systems.
  • Awareness of model-based systems engineering processes, tools (e.g. Cameo Systems Modeller,) and languages (e.g. SysML).
  • Awareness of agile development methodologies.
You can not apply for this job as its status is Closed.
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