The Team and Role:
As an Audio ML Data Engineer on the Logitech Hardware Audio ML and DSP Product team, you will work on developing and managing our audio datasets and data pipelines. This work directly influences the innovative audio experiences we deliver to our customers.
The Audio ML Data Engineer's key responsibilities include:
- Data Pipeline Management: Ensuring the integrity and quality of Audio ML data pipelines and datasets, which involves robust data augmentation and managing workflows for supervised, unsupervised, and semi-supervised ML audio applications.
- Model Development and Deployment: Collaborating with the team to develop and deploy Audio ML models, specifically targeting platforms with strict resource limitations (such as Tensilica DSP, ARM, and RISC-V).
Your Contribution:
Be Yourself. Be Open. Stay Hungry and Humble. Collaborate. Challenge. Decide and just Do. Share our passion for Equality and the Environment. These are the behaviors and values you’ll need for success at Logitech. In this role you will:
- Design and manage audio data collection, curation, labeling, cleaning and augmentation pipelines
- Evaluate and implement scalable data augmentation techniques.
- Establish and maintain high-quality, well-versioned, and documented datasets essential for training, validation, and benchmarking of audio ML models.
- Build automated tools for monitoring and ensuring the quality and statistical diversity of audio data.
- Formulate and execute strategies for continuous improvement of existing datasets.
Key Qualifications:
For consideration, you must bring the following minimum skills and experiences to our team:
- Audio Data Expertise: A minimum of 3 years of direct experience working with extensive audio datasets, including advanced data augmentation and preprocessing techniques audio ML.
- Python Proficiency: Strong proficiency in Python for both ML model development and automating data pipelines.
- Data Pipelines and ML Frameworks: Proven expertise in building scalable data pipelines and expertise in employing ML frameworks (TensorFlow, Keras) with large-scale, complex datasets
Preferred Qualifications:
- Expert-level skills in audio analysis, including listening and artifact detection, with a proven track record of validating performance across diverse datasets.
- Strong familiarity with designing, executing, and statistically analyzing audio quality measurement protocols, specializing in managing data-driven objective and subjective evaluations.
- A strong data-first mindset, with a demonstrated ability to drive innovation both independently and as part of a team.
- Proficiency in C and SQL, along with experience using code version control systems (Git), is a valuable asset.
- Excellent cross-functional communication, documentation, and leadership skills, emphasizing transparency in data and results.
Education:
- Bachelor’s or Master’s degree in Electrical Engineering, Computer Science, or a related discipline.
- Equivalent practical experience in professional audio ML and data engineering considered; advanced/relevant continuing education preferred.
