Description

You will develop and implement machine learning models for audio signal processing, focusing on source separation and advanced filtering techniques.

Responsibilities

• Apply deep learning techniques to audio signal processing tasks.
• Solve source separation problems using advanced algorithmic approaches.
• Implement auto-filtering and Wiener filtering techniques.
• Utilize Short Term Fourier Transforms (STFT) in signal analysis and processing.

Required Skills

• 5+ years of experience developing and implementing machine learning models and algorithms.
• 3+ years of experience with acoustics, sound, and audio signals.
• Proficiency in C/C++ or Python.
• Experience with TensorFlow or PyTorch frameworks.
• Demonstrated experience with the Source Separation Problem.
• Hands-on experience with Short Term Fourier Transforms applications.
• Practical knowledge of Auto-Filtering and Wiener Filtering implementation.
• Masters, PhD, or advanced training in applied mathematics, engineering, or computer science.

Preferred Skills

• Ability to work within California time zones for team alignment.

Key Skills

Education

Any Graduate