Acoustics Machine Learning Data Engineer
3 Days Old
- Design, build, and maintain scalable and efficient data pipelines for processing large-scale audio and acoustic datasets.
- Collaborate with ML researchers and acoustic scientists to collect, annotate, transform, and curate high-quality training and evaluation datasets.
- Implement signal processing algorithms for feature extraction
- Work on real-time and batch processing frameworks for streaming and static audio data.
- Support model training and evaluation through optimized data loaders and preprocessing steps.
- Ensure data quality, versioning, and reproducibility using best practices in data engineering.
- Deploy and maintain cloud-based infrastructure for data workflows (e.g., AWS, GCP, Azure).
- Develop tools for data visualization and annotation specific to acoustic events.
Required Qualifications
- Bachelor's or Master's degree in Computer Science, Electrical Engineering, Acoustics, or a related field.
- Strong experience with audio signal processing libraries (e.g., Librosa, PyDub, SciPy, torchaudio).
- Proficient in Python and relevant data engineering frameworks (e.g., Airflow, Apache Beam, Spark).
- Experience working with large-scale data pipelines and cloud infrastructure.
- Familiarity with machine learning workflows, especially in audio or time-series domains.
- Understanding of acoustic features and formats (e.g., WAV, FLAC, sampling rates).
- Strong knowledge of databases, data storage formats (e.g., Parquet, HDF5), and data management tools.
Preferred Qualifications
- Experience with deep learning frameworks (e.g., PyTorch, TensorFlow) for audio modeling.
- Knowledge of acoustic modeling, speech recognition, or sound classification.
- Experience with edge deployment and real-time audio processing.
- Familiarity with tools like Weights & Biases, MLflow, or DVC for ML operations.
- Location:
- Cupertino
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