Audio Machine Learning Data Engineer

Who we are

As a company, we're just the right size for every person to take initiative and make things happen. We're big enough in our portfolio for those actions to have a global impact. That's a pretty sweet spot to be in and we strive to keep it that way.

Your Contribution

Data Pipeline Management: Ensuring the integrity and quality of Audio Machine Learning data pipelines and datasets, which involves robust data augmentation and managing workflows for supervised, unsupervised, and semi-supervised Machine Learning audio applications. Model Development and Deployment: Collaborating with the team to develop and deploy Audio Machine Learning models, specifically targeting platforms with strict resource limitations (such as Tensilica DSP, ARM, and RISC-V). Design and manage audio data collection, curation, labeling, cleaning and augmentation pipelines Evaluate and implement scalable data augmentation techniques. Establish and maintain high-quality, well-versioned, and documented datasets essential for training, validation, and benchmarking of audio Machine Learning models. Build automated tools for monitoring and ensuring the quality and statistical diversity of audio data. Formulate and execute strategies for continuous improvement of existing datasets.

Key Qualifications

For consideration, you must bring the following minimum skills and experiences to our team: Audio Data Expertise: A minimum of 3 years of direct experience working with extensive audio datasets, including advanced data augmentation and preprocessing techniques audio Machine Learning. Python Proficiency: Strong proficiency in Python for both Machine Learning model development and automating data pipelines. Data Pipelines and Machine Learning Frameworks: Proven expertise in building scalable data pipelines and expertise in employing Machine Learning frameworks (TensorFlow, Keras) with large-scale, complex datasets

Fähigkeiten

PythonML / AITensorFlowKerasCSQLGit

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