|Published||March 19, 2023|
|Location||Egham, United Kingdom|
We encourage enthusiastic researchers and engineers with a strong academic background and expertise in development of audio/speech related applications to apply. You will be poised to grow and expand your programming skills and expertise into a dynamic new set of research problems. This position will require you to work closely with researchers and engineers to enable and accelerate new research efforts in AI.
The role is based at our Research & Development Institute in West London, UK. We adopt a hybrid working model of 3 days working from the office and 2 days from home weekly.
Role and Responsibilities
As a Machine Learning Research Engineer, you will:
Research, design and develop state-of-the-art deep-learning and On-Device (privacy preserving and personalisation) methods Research and develop innovative AI models for Speech, Language and other domains as needed Develop, test and deploy solutions on flagship mobile devices Build high quality and maintainable code following best software development practices Have technical responsibility for one or more significant sections of the assigned research project.
Collaboratively work with a dynamic team with varied research & development backgrounds
Skills and Qualifications
MSc/PhD degree in Artificial Intelligence, Computer Science/Engineering, Electronics, Mathematics, or related disciplines Professional software development experience with C++/ Python Excellent knowledge of fundamentals of machine learning and deep-learning concepts A proven track record in AI model development and deployment (on embedded devices is a plus) Experience with programming using machine learning frameworks such as Tensorflow or PyTorch Good familiarity with relevant python libraries (such as NumPy) and tools (such as TensorBoard).
Excellent communication, team work and a results-oriented attitude.
Proficiency in problem solving and debugging.
Experience and expertise in Speech Processing, and Language Modelling applications (e.g. voice assistants, speech recognition, NLP/NLU, TTS, etc.) Experience developing production AI training pipelines and working with distributed ML systems Knowledge of advanced AI methods and algorithms Experience with deploying AI models on Android/mobile devices.
Publications in top ML/AI conferences (e.g. ICML, NeurIPS, Interspeech, SysML or similar).
Contribution to open source ML frameworks such as -TensorFlow, TensorFlow Lite, etc.