Data Engineer

Rose International

Cupertino, CA

Temporary

Mid Level

13 days ago

Job Description

About the Role

The Data Engineer role is based in Cupertino, CA, and involves designing, building, and maintaining scalable data pipelines for audio and acoustic datasets. The position requires collaboration with ML researchers and acoustic scientists to support audio modeling and processing workflows, leveraging cloud infrastructure and data management tools. It is a full-time, temporary role with an emphasis on large-scale data processing and real-time audio data handling.

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.

Requirements

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.

Nice to Have

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.

Benefits & Perks

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