Remote Audio ML Data Engineer — Pipelines & Models

El Camino Health; Lausanne; Hybrid; CHF 120,000 - 180,000; Full time

Benefits offered by this job

Hybrid work model

Job summary

Logitech is seeking an Audio Machine Learning Data Engineer to develop and manage audio datasets and data pipelines for our hardware audio ML and DSP products. You will influence audio experiences by ensuring dataset quality, building scalable augmentation, and deploying ML models on constrained platforms.

You will work within a team focused on data integrity, reproducibility, and cross-functional collaboration, with a hybrid work model allowing remote and on-site collaboration globally.

Qualifications

• 3+ years of experience with large audio datasets and data augmentation/preprocessing.

Responsibilities

• Data Pipeline Management: ensure integrity and quality of audio ML data pipelines and datasets, including data augmentation; manage supervised/unsupervised/semi-supervised workflows.
• Model Development and Deployment: develop and deploy audio ML models for resource-constrained platforms (Tensilica DSP, ARM, RISCV).
• Design and manage audio data collection, labeling, cleaning and augmentation pipelines; evaluate scalable augmentation techniques.
• Establish and maintain high-quality, versioned datasets for training, validation and benchmarking.
• Build automated tools for monitoring data quality and diversity.
• Formulate strategies for continuous improvement of datasets.

Skills

Audio data

Python

Data pipelines

ML frameworks

Education

Bachelor's or Master's in EE/CS

Tools

TensorFlow

Keras

Job description

Logitech is seeking an Audio Machine Learning Data Engineer to develop and manage audio datasets and data pipelines for our hardware audio ML and DSP products. You will influence audio experiences by ensuring dataset quality, building scalable augmentation, and deploying ML models on constrained platforms.

You will work within a team focused on data integrity, reproducibility, and cross-functional collaboration, with a hybrid work model allowing remote and on-site collaboration globally.