Audio Machine Learning Data Engineer

Logitech, a global creator of award‑winning gaming, productivity and connectivity products, is looking for an Audio Machine Learning Data Engineer to join its Hardware Audio Machine Learning and DSP Product team. The role focuses on shaping the audio datasets and pipelines that power the company’s innovative sound experiences. Each day you will be responsible for keeping audio data pipelines reliable and high‑quality, applying robust augmentation, and handling workflows for supervised, unsupervised and semi‑supervised machine‑learning tasks. You will design and run processes for gathering, curating, labeling, cleaning and augmenting audio data, evaluate scalable augmentation methods, and maintain well‑versioned, documented datasets used for training, validation and benchmarking. In addition, you will build automated monitoring tools to assure statistical diversity and continuously improve existing collections, while collaborating on the development and deployment of models for tightly constrained platforms such as Tensilica DSP, ARM and RISC‑V. The minimum qualifications include at least three years of hands‑on work with large audio datasets, advanced augmentation and preprocessing for machine‑learning, strong Python skills, and proven ability to construct scalable pipelines using frameworks like TensorFlow or Keras on complex data. A bachelor’s or master’s degree in electrical engineering, computer science or a related field (or equivalent professional experience) is required. Preferred experience covers expert audio analysis, listening for artifacts, and validating performance across varied sets, as well as designing and statistically analysing audio quality tests. Familiarity with C, SQL, Git, and strong cross‑functional communication and documentation abilities are valued. Logitech offers a hybrid work model that supports remote work from most locations, an inclusive culture that celebrates diversity, and flexible, comprehensive benefits designed to promote physical, financial, emotional, intellectual and social wellbeing.