Audio Machine Learning Co-op

Description:

• A substantial portion of your time will be devoted to prototyping and implementing ML algorithms, evaluating them against objective and perceptual metrics, helping curate and develop internal audio datasets, and regularly presenting your results.
• You will integrate your novel solutions into new and existing systems and platforms to deliver new proofs of concept.
• You will have a chance to contribute to projects, which will be shipped to Bose customers, apply for patents or submit papers to top-tier AI and signal processing conferences (e.g., NeurIPS, ICASSP, Interspeech, etc.).

Education

• Pursuing or recently finished a degree in ML, Computer Science, Music Technology or a related field. Graduate-level candidates are preferred, but the position is open to any student or recent grad with a suitable skill set, interest and motivation.
• At a minimum, the candidate should be familiar with the material covered in introductory courses in digital signal processing, linear algebra, statistics, and data structures.

Skills

• Practical knowledge of applied ML and digital signal processing (DSP).
• Experience developing ML algorithms in Python, TensorFlow/PyTorch and/or C/C++.
• Familiarity with methods for microphone array signal processing (e.g., beamforming, acoustic echo cancellation/dereverberation, sound event localization, etc.).
• Familiarity with at least one of the following research topics: source separation, speech enhancement, spatial audio synthesis, room acoustics analysis/simulation, and/or model compression/pruning.
• Familiarity with some of the following technologies is also preferred: TFLite, Ambisonics surround sound, AWS stack.
• Familiarity with standard version control practices.
• Strong communication skills. You will present your work internally to a large, interdisciplinary audience on a regular basis.