Machine Learning Engineer (27083)

This growing start-up is developing speech authentication technologies based on AI algorithms which are streamlined enough to execute on edge devices. With their technology development now ramping up, they are keen to recruit a Machine Learning Engineer to join their team.

The role will involve you in finding ways to optimise deep learning models for execution time, memory usage etc. in order that they can execute on resource-constrained Edge AI processors. A key part of the team, you could be working on the development and optimisation of machine learning algorithms, collaborating closely with colleagues from disciplines such as embedded software engineering, to turn research models into working products. You’ll be involved throughout the SDLC, developing commercial-quality software, analysing data to obtain insights for informing future development iterations, assisting with deployment of software and characterising algorithm performance.

You will need:

• An excellent academic background in computer science or a related subject with at minimum a 1st or 2.1 degree from a leading university together with a relevant higher MSc or PhD and strong supporting A-level (or equivalent) grades.
• Expansive machine learning knowledge, including familiarity with deep learning architectures, and expertise in speech modelling, recognition, or diarisation.
• An understanding of acoustic modelling and signal processing.
• Skills in Python, including toolkits such as TensorFlow, PyTorch, NumPy, Pandas, TFlite, ONNX, and Scikit-Learn

Applications for edge-based AI are far-reaching, and you would be joining this ambitious and innovative company at an exciting stage in their development. A competitive salary is on offer to the successful candidate. The company are based in central London, but the role can be substantially remote, possibly fully remote.

Keywords: Machine Learning, Artificial Intelligence, Edge Computing, Speech Recognition, Deep Learning, TensorFlow, Python, TinyML, ONNX, London

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