Audio Machine Learning Engineer
- Collaborate with cross-functional teams to design and implement machine learning models and algorithms for audio processing, analysis, and enhancement.
- Train, validate, and fine-tune machine learning models for various applications.
- Evaluate and benchmark the performance of machine learning models using appropriate metrics and statistical techniques.
- Collaborate with software engineers to integrate machine learning algorithms into audio software products and ensure seamless functionality and performance.
- Debug and solve issues related to machine learning algorithms and audio software applications.
- Document software development processes, algorithms, and experiments, and communicate findings and recommendations to the team effectively.
- Ph.D. in relevant field with 0+ years or Masters in relevant field with 3+ years of experience in developing and deploying machine learning models for audio-related applications.
- Must have Strong programming skills in Python and Matlab, with experience in audio processing libraries (e.g., librosa, torch audio, or similar).
- Must understand machine learning techniques, including deep learning architectures (e.g., CNNs, RNNs, GANs) and relevant frameworks (e.g., PyTorch).
- Must be proficient in data preprocessing, feature extraction, and data augmentation techniques for audio.
- Expecting to have strong problem-solving skills and ability to think creatively to devise innovative solutions to audio-related challenges is required.
- Familiarity with audio signal processing concepts, such as Fourier analysis, spectral modeling, and time-frequency representations is essential.
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