Senior AI Research Scientist

Job Description
We are seeking a highly skilled Senior AI Research Scientist to join our groundbreaking audio technology research team in London. You will have the opportunity to be part of building new, exciting, audio technologies that will be used by thousands of people across the world.
You will contribute to our R & D track by owning the evolution of our leading real time machine learning audio algorithms. If you are an experienced ML/AI Researcher with a strong academic background and prior experience using a variety of ML models on music, speech, or audio data give us a shout!
Key aspects of the role will include:
Conducting research on deep learning applied to audio, going beyond the state of the art
Evaluating and stress-testing AI/ML models to ensure they are real-world ready and suitable for production
Optimising and shrinking ML models to enable them to operate with minimal memory needs and low processor overhead in embedded environments
Working with ML engineers to build exceptional real time audio deep learning algorithms
Covering all aspects of modern ML workloads, including model research, implementation, training, evaluation, inference
Communicating results internally with colleagues from different teams, and externally
Writing internal reports and research papers
Requirements:
MSc/PhD degree in Artificial Intelligence, Machine Learning, Computer Science/Engineering, Mathematics, or related discipline
Extensive knowledge of Machine Learning and deep-learning concepts
Strong experience in network miniaturisation using techniques such as knowledge distillation
Knowledge in implementing custom operators for deep learning framework (TF-light, GGML, ONNX, ...)
Excellent Python programming skills
Expert in at least one popular machine learning framework such as Pytorch
Knowledge of real-time audio signal processing and/or real-time machine learning
Proven experience of applying and optimising AI/ML techniques and algorithms to meet the needs of specific practical problems
Prefered:
Team player
Background in music or audio