Senior Machine Learning Engineer Ajouter aux favoris
- Work in the Audio Machine Learning team of Sonos Voice Control, together with a group of experienced machine learning engineers and researchers
- Design and train state-of-the-art machine learning models for automatic speech recognition and wakeword detection
- Ensure our models perform the best they can on specialised domains, such as music entities, loudspeaker control and home automation
- Define and implement the data strategy for training and testing, as well as audio augmentation to reflect the far-field acoustic conditions of our products
- Maintain and improve scalable and efficient training and evaluation pipelines
- Contribute to the team's roadmap and set the technical direction for the ASR domain
- Mentor other team members on ML best practices, experiment planning, and model analysis
- Collaborate with the cloud backend and embedded engineering teams to ensure our models perform the best they can in the different environments
- 8+ years experience in machine learning research & engineering for voice applications
- A PhD or Master's degree in computer science, or a related technical field (or equivalent experience)
- In-depth knowledge of speech processing (ASR, wakeword detection, audio features), particularly latest architectures for automatic speech recognition (RNN-T, Transformers)
- Experience in owning the whole model development lifecycle from data generation, training, evaluation to shipping models for production
- Advanced knowledge of Python and common machine learning toolkits (PyTorch)
- Intermediate knowledge of a low-level compiled language (Rust, C, C++)
- Experience in developing models for real-time, low latency, streaming ASR
- Experience in model adaptation techniques for large entity catalogs (shallow-fusion biasing, rescoring, error correction)
- Understanding of the particular challenges of far-field ASR, including acoustics and data augmentation for model training and evaluation
- MLOps and software engineering for efficient training and data infrastructure
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