Audio Machine Learning Co-Op
Description
Job Description
THE PROGRAM
We're looking for students to join our Co-Op Program who are obsessively curious about 'what's next'. You'll get hands on experience with our products and learn from the best of the best in the business. You will immerse yourself into Bose for 6 months and get the opportunity to grow the skills your learned in the classroom with hands on work experience. By the time you end your time with us, you will have been given the opportunity to truly make a real impact in the future of Bose and your career!
Opportunities don't stop at your day-to-day work. While you're getting an in depth look at your area of expertise, we'll expose you to other areas of the company. Our Co-Ops are given the opportunity to connect with senior leadership across the business to understand different perspectives at Bose. You'll network with other Co-Ops and colleagues to grow your network for the future!
Timeframe - July-December 2025
THE ROLE
The goal of our 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, digital signal processing, software engineering and psychoacoustics to prototype and implement new algorithms.
Current research interests of our group include machine learning (ML) methods for single- and multi-channel audio signal processing, source separation, perceptually-grounded speech enhancement, lightweight, low-latency ML models for low-resource devices, and related topics.
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
- The majority of your time will be devoted to prototyping, implementing and evaluating ML algorithms, curating and developing internal resources, and regularly presenting your findings.
- You will integrate your novel solutions into new and existing systems and platforms to deliver new proofs of concept.
- You will be able 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.).
Requirements
Education
Pursuing or recently finished a graduate-level degree in ML, Computer Science, Music Technology or a related field.
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 audio ML and digital signal processing (DSP).
- Hands-on experience with at least one of the following research topics: source separation, speech enhancement, microphone array signal processing (e.g., beamforming), generative audio modelling, model compression (e.g., quantization, pruning).
- Familiarity with methods for spatial audio synthesis and/or room acoustics analysis/simulation is a plus.
- Experience developing ML algorithms in Python, TensorFlow/PyTorch and/or C/C++.
- Familiarity with standard version control practices.
- Familiarity with the following technologies is also preferred: TFLite, ONNX and/or AWS stack.
- Strong communication skills. You will present your work internally to a large, interdisciplinary audience on a regular basis.
How to apply
Apply via link
Bose Corporation
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Bose Corporation was founded in 1964 by Dr. Amar G. Bose, then a professor of electrical engineering at the Massachusetts Institute of Technology. Today, the company is driven by its founding principles, investing in long-term research with one fundamental goal: to develop new technologies with real customer benefits. Bose innovations have spanned decades and industries, creating and transforming categories in audio and beyond. Bose products for the home, in the car, on the go and in public spaces have become iconic, changing the way people listen to music.