2 of 2 Machine Learning Engineer Jobs in Cambridgeshire

platforms. About the Role Roku is building a world class voice system that is used by millions of Roku users. The Roku Voice team is looking for ambitious experienced machine learning engineers with a background in one or more of the following areas: artificial intelligence, natural language understanding, machine learning, automated speech recognition, conversational systems and … make pragmatic technical decisions Qualifications 5+ years hands on experience in building challenging production systems Strong CS fundamentals, with the ability to write algorithms with ease Good understanding of machine learning fundamentals like regression, classification, ranking, neural networks, and sequence models for text/speech Experience with advanced machine learning techniques, including LLM and generative AI … Proven familiarity and experience deploying and maintaining machine learning models (transformer based models in production is a plus), including identifying the right KPIs and objective functions Experience working with big data systems (Spark, EMR, S3, Airflow) and programming languages (Java, Python, C++) Experience building in-production NLU and/or ASR systems Bachelor Degree required. MS in Computer 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 ❯