Acoustics Machine Learning Data Engineer, Cupertino

Acoustics Machine Learning Data Engineer

New Yesterday

Key Responsibilities

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.

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