Job description

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 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 vary. In 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. ‎

Requirements

Must-haves

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.
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

Nice-to-haves

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

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

machine learningmlpython

Platform: workday
Effort: long

Benefits

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401(K)