ML Data Engineer A globally leading technology company is looking for an ML Data Engineer to design and optimize data pipelines for large-scale audio and acoustic machine learning workflows. In this role, you’ll collaborate with researchers and signal processing experts to deliver high-quality, scalable datasets that power state-of-the-art models. If you're passionate about machine learning infrastructure, audio data, and real-world impact, we’d love to hear from you. 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. • 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. Type: Contract Duration: 7 months (with a possibility to extend to 18 months) Work Location: Cupertino, CA (100% On site) Pay Rate: $ 45.00 - $ 60.00 (DOE) No C2C or third party agencies