Audio Machine Learning Engineer Co-op

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, TensorFlow, 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