Embedded Audio ML Engineer Co-op
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.
Spring Co-op: Candidates must be available to work full-time (40 hours per week) in a hybrid, in-office format from January through July, 2027. 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., SpeechClarity) to audio perception. As an Embedded Audio ML Engineer Co-op at Bose, you will help deploy and optimize our next generation deep learning algorithms for resource-constrained devices.
Responsibilities
- Build embedded software prototypes to demonstrate new ML-powered audio features on our wearables
- Design, implement, deploy and optimize deep learning models, data structure and algorithms on resource-constrained devices.
- Research, design and implement novel methods for efficient deep learning (e.g., quantization, pruning, NAS, knowledge distillation) with a focus on audio-related problems.
- Train and evaluate deep learning models to enable new experiences on our headphones, earbuds, soundbars, portable speakers and other consumer audio devices.
- Evaluate neural network hardware accelerators for low-power wearable devices, including working with various vendors’ custom toolchains, software and hardware and informing leadership teams about tradeoffs.
Minimum Qualifications
- Currently pursuing a M.S. or Ph.D. in Computer Science, Electrical Engineering, Machine Learning, or a related field
- Strong proficiency in C/C++ for embedded systems.
- Proficiency in Python for model conversion, evaluation, or tooling workflows.
- 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
- Experience deploying ML models on edge or resource‑constrained devices (MCU/DSP/NPU).
- Solid background in audio deep learning and digital signal processing