Audio Machine Learning Co-op

Candidates must be available to work full-time (40 hours per week) from July 13 through December 18, 2026. No relocation assistance is available.

THE ROLE

The goal of the Audio Machine Learning Research team is to develop novel AI-powered audio processing algorithms. The twist is that our algorithms must run in real-time, on physical devices, for applications such as voice pickup, hearing augmentation and ones we haven't even thought of yet. As part of the team, you will work with experts in machine learning (ML), digital signal processing (DSP), software engineering and psychoacoustics to prototype and implement new algorithms.

Bose has a strong history of combining creative thinking with cutting-edge technology in the audio domain. We are looking for candidates passionate about machine learning and audio to help us shape the next chapter in the future of Bose!

Responsibilities:

• Most of your time will be devoted to prototyping, implementing and evaluating ML algorithms, curating and developing internal resources, and presenting your findings.
• You will integrate your novel solutions into existing systems and platforms to showcase new (proof of concept) solutions.
• You will be able to contribute to projects, which will be shipped to Bose customers, apply for patents, and/or submit papers to top-tier AI and signal processing conferences (e.g., NeurIPS, ICASSP, Interpeech, etc.).

Education:

• Pursuing or recently finished a graduate-level degree in ML, Computer Science, Music Technology or a related field.

Skills:

• Practical knowledge of Applied audio ML (TensorFlow/PyTorch, TFLite/ONNX is a plus) and Audio DSP (Python, Matlab and/or C/C++).
• Hands-on experience in at least one of the following research topics: Audio source separation, Speech enhancement, Microphone array signal processing, Tiny ML, Generative audio modelling
• Familiarity with methods for spatial sound synthesis and/or room acoustics simulation/analysis is a plus.
• Strong communication skills. You will be presenting your work to a large interdisciplinary community.