Audio Machine Learning Intern

You know the moment. It’s the first notes of that song you love, the intro to your favorite movie, or simply the sound of someone you love saying “hello.” It’s in these moments that sound matters most. 

At Bose, we believe sound is the most powerful force on earth. We’ve dedicated ourselves to improving it for more than 60 years. And we’re passionate down to our bones about making whatever you’re listening to a little more magical.  

The Engineering team at Bose is a thriving, passionate, deeply skilled team of professionals from a broad range of disciplines and experiences, who share a common goal—to create products that provide transformative sound experiences.

Job Description

Timeframe: June 1 – August 21, 2026

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.

Bose is an equal opportunity employer. We evaluate qualified applicants without regard to race, color, religion, sex, sexual orientation, gender identity, genetic information, national origin, age, disability, veteran status, or any other legally protected characteristics. The EEOC’s “Know Your Rights: Workplace discrimination is illegal” Poster is available here: https://www.eeoc.gov/sites/default/files/2023-06/22-088_EEOC_KnowYourRights6.12ScreenRdr.pdf. Bose is committed to providing reasonable accommodations to individuals with disabilities. If you require reasonable accommodation in completing this application, interviewing, completing any pre-employment testing, or otherwise participating in the employee selection process, please direct your inquiries to [email protected]. Please include "Application Accommodation Request" in the subject of the email.

Our goal is to create an atmosphere where every candidate feels supported and empowered in the interviewing process. Diversity and inclusion are integral to our success, and we believe that providing reasonable accommodation is not only a legal obligation but also a fundamental aspect of our commitment to being an employer of choice. We recognize that individuals may have different needs and requirements based on their abilities, and we provide reasonable accommodations to ensure ideal conditions are met during the application process.