Audio Machine Learning Engineer Co-op

Audio Machine Learning Engineer Co-op page is loaded## Audio Machine Learning Engineer Co-op locations:
US, MA - Framingham time type:
Full time posted on:
Posted Yesterday time left to apply:
End Date:
March 27, 2026 (30+ days left to apply) job requisition :
R28719

At Bose Corporation, we believe sound is the most powerful force on earth — and for over 60 years, we have been a company built on innovation, excellence, and independence. Privately owned, fiercely customer-focused, and driven by our values, we continue to lead industries and transform lives through sound.
Today, Bose Corporation is entering an exciting new era. Across multiple global Business Units and Global Functions, we are shaping the future of audio technology, automotive, luxury, and premium experiences. We invite you to join us in this transformation. #
** Job Description
**** ABOUT THE CO-OP PROGRAM
** Bose Corporation is seeking students to participate in our Co-op Program who share our belief that sound is power. During the six-month co-op, students will have the chance to apply classroom learning in a hands-on work environment. Co-op participants will network across the business to gain diverse perspectives within Bose Corporation. Additionally, co-ops will connect with other students and colleagues, building valuable professional relationships for the future.

Fall Co-op:
Candidates must be available to work full-time (40 hours per week) in a hybrid, in-office format from July 13 through December 18, 2026. No relocation assistance is available.
** THE ROLE
** At Bose, we use machine learning across a diverse set of problems ranging from speech enhancement (e.g., Bose PinPoint) to audio perception. As an ML Engineer Co-op at Bose, you will help build our next generation of wearable and speaker products empowered by deep learning.
** Responsibilities
* ** Build desktop and embedded software prototypes to demonstrate new ML-powered audio features on our wearables
* Design, train, and evaluate deep learning algorithms with a focus on low-latency, on-device audio tasks
* Research, implement, and benchmark state-of-the-art approaches in audio ML, speech enhancement, keyword spotting, and audio signal processing
* Collaborate across hardware, firmware, and UX teams to define requirements and inform product roadmaps

Minimum Qualifications
* Currently pursuing a M.S. or Ph.D. in Computer Science, Electrical Engineering, Machine Learning, or a related field
* 3+ years of Python and C/C++ experience
* Strong experience implementing deep neural networks with PyTorch, Tensor Flow, or similar frameworks
* Proven cross-group and cross-culture collaboration skills
* Creative problem-solver with demonstrated ability to work independently

Preferred Qualifications
* Prior internship or work experience as a Software/ML engineer
* Publication record in ML or audio signal processing venues
* Solid background in digital signal processing and embedded systems

At Bose, you're inspired to be and do your best and are rewarded for your unique talents! Our compensation is thoughtfully tailored to your skills, experience, education, and location, and goes beyond base salary. The hiring range for this position in the primary work location of Framingham, Massachusetts is: $40.00-$51.25 per hour.

The hiring range for other Bose work locations may  addition to competitive base pay we offer rewards including bonus programs, comprehensive health and welfare benefits, a 401(k) plan, plus exclusive perks designed to support your wellbeing, and a generous employee discount where you can immerse yourself in our products and experiences. We are a proudly independent company—driven by purpose, guided by our values, and united by a belief in the power of sound.

As the world leader in audio experiences, we’re creating what’s next—pushing boundaries and delivering transformative sound experiences for people everywhere. Join us and make your next career move a mic-drop. Let’s Make Waves.

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,…