2 of 2 Machine Learning Engineer Jobs in the East of England

We are hiring a Principal Machine Learning Engineer to work on cutting-edge Research & Development. As innovators in speech technology, our mission is to Understand Every Voice-a vision that has propelled us to be world leaders of Voice AI, with STT, TTS, and Flow; our brand new Conversational AI platform. Fuelled by innovation, inclusivity, and a … passion for making a global impact through world-leading Speech AI, we're looking for an experienced Machine Learning Engineer to accelerate our efforts towards exceptional speech solutions. Our Modelling Team trains diverse models, including large self-supervised ones, supporting Speechmatics towards being the most accurate speech recognition system globally. It also ensures their deployment into production … groups of engineers to achieve ambitious goals; you will work on complex R&D projects, and guide others by defining research milestones and collaborating together. Raise the bar for machine learning at Speechmatics, by applying your innovative ideas and experiences to improve the efficiency, effectiveness, and best practices across all of our machine learning teams. Set More ❯

Speechmatics is a cutting-edge applied AI Research company that is breaking down cultural barriers by building diverse and inclusive speech technology. We are looking for an experienced Machine Learning Engineer who can help us advance our automatic speech recognition (ASR) engines for Flow, our new Conversational AI companion and create interactive voice interfaces of the future … of audio and billion parameter modelswhich train across dozens of GPUs. It's all about finding bottlenecks across our 50+ languages. Our main research focuses arelarge-scale self-supervised learning, building state-of-the-art speech pipelines and breaking ground on our own emotive text-to-speech (TTS) models. What you'll be doing Working with a diverse group … of engineers across Speechmatics. Scaling self-supervised learning models across hundreds of GPUs in the cloud. Experimenting with distillation or quantisation to speed up our models at runtime. Comparing compute efficiencies of architectures such as a transformer and the impact on WER. Developing new, state-of-the-art AI features, from training models, all the way to shipping it More ❯